Dissertations / Theses on the topic 'Smart Manufacturing Systems'

The world of industrial manufacturing has changed a lot during the past decades. It has gone from a labour-intensive process of manual control of machines to a fully connected and automated process. The next big leap in industrial manufacturing is known as industry 4.0 or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor. This change has proven challenging to implement into existing factories many with the intended lifespan of several decades. One of the single most important parameters to measure is the operating hours of each machine. This information can help companies better utilize their resources and save huge amounts of money.  The goal is to develop a solution which can track the operating hours of the machines using image analysis and the signal lights already mounted on the machines. Using methods commonly used for traffic light recognition in autonomous cars, a system with an accuracy of over 99% during the specified conditions, has been developed. It is believed that if more diverse video data becomes available a system, with high reliability that generalizes well, could be developed using similar methodology.
Industriell tillverkning har förändrats mycket under de senaste decennierna. Det har gått från en process som krävt mycket manuellt arbete till en process som är nästan helt uppkopplad och automatiserad. Nästa stora steg inom industriell tillverkning går under benämningen industri 4.0 eller smart tillverkning. Med industri 4.0 kommer en ökad integration mellan IT-system och fabriksgolvet. Denna förändring har visat sig vara särskilt svår att implementera i redan existerande fabriker som kan ha en förväntad livstid på flera årtionden. En av de viktigaste parametrarna att mäta inom industriell tillverkning är varje maskins operativa timmar. Denna information kan hjälpa företag att bättre utnyttja tillgängliga resurser och därigenom spara stora summor pengar. Målet är att utveckla en lösning som, med hjälp av bildanalys och de signalljus som maskinerna kommer utrustade med, kan mäta maskinernas operativa timmar. Med hjälp av metoder som vanligen används för trafikljusigenkänning i autonoma fordon har ett system med en träffsäkerhet på över 99% under de förutsättningar som presenteras i rapporten utvecklats. Om mer video med större variation blir tillgänglig är det mycket troligt att det går att utveckla ett system som har hög pålitlighet i de flesta produktionsmiljöer.

This thesis is devoted to the study of the optimisation-based control techniques for the design of control strategies that contribute to improve the energy efficiency of smart manufacturing systems. Currently, manufacturing industry is suffering a transformation towards smart, flexible, and energy-efficient manufacturing systems. This transformation requires modularised and reconfigurable manufacturing systems to respond to changes in productions programs and to the time-varying pieces demand while keeping an energy-efficient operation. Thus, suitable control systems should be designed to satisfy the requirements of this transformation while minimising the energy consumption and maximising the plant profit. In this regard, optimisation-based controllers are suitable for the design of control systems that minimise the total energy consumption of such systems while remaining their productivity taking into account the operational conditions and the factors that affect them. First, this dissertation presents how optimisation-based control techniques can contribute to face the challenges of the smart manufacturing systems. Based on this review, manufacturing industry is classified by levels, i.e., machine, process line, and plant levels, for the design of optimisation-based controllers. Besides, to design control strategies that do not affect plant productivity, manufacturing systems are also classified according to the operations performed. Based on these classifications, control strategies are proposed to minimise either the total energy consumption of manufacturing systems or the energy costs related to the operation of such systems. At both machine and process line levels, control strategies are designed based on model predictive control approach to minimise their energy consumption. The underlying idea behind the proposed control strategies consists of managing independently those devices (or systems) that are not directly involved in the machining operations. Thus, energy consumption models are required to predict the total energy consumption profile of manufacturing systems and, based on this, to select the activation/deactivation instants of the manipulated devices that minimise their energy consumption and guarantee their proper operation. Next, due to at the process line level the size and complexity of manufacturing systems increases, a control strategy based on two control modes is proposed to reduce the computational burden of such controllers by switching from a control mode based on online optimisation to an autonomous control mode without solving an optimisation problem. Since the need for flexible and reconfigurable manufacturing systems, non-centralised control strategies are proposed at higher industrial levels to minimise their energy consumption. Thus, both cooperative and non-cooperative local controllers are designed considering a fixed system partitioning and using alternative direction methods of multipliers to solve the optimisations problems in a distributed fashion. Besides, due to the nature of the proposed control objectives, a way to define the consensus stage among the local controllers with coupled dynamics is proposed. Finally, the control strategies designed at plant level are based on the economic model predictive control approach to maximise the plant profit and minimise the operational costs related to the plant operation. At this level, control objectives are focused on determining the economic-optimal production programming of the plant that the control strategies at lower levels should follow. In this regard, the production programming of the plant is determined taking into account the pieces demand, the energy consumption of manufacturing systems, and the current energy market and their fluctuations. All control strategies proposed in this thesis are tested in simulation considering different scenarios designed based on the real operation of an automotive part manufacturing plant.
Esta tesis se basa en el estudio de las técnicas de control basadas en optimización para el diseño de estrategias de control que mejoren la eficiencia energética de los sistemas de manufactura inteligentes. La industria de manufactura se está transformando hacia sistemas de manufactura inteligentes, flexibles y eficientes energéticamente, que requiere de estructuras modulares y reconfigurables para poder responder a los cambios en la programación de la producción y la demanda de piezas. Así, se deben diseñar sistemas de control que cumplan los requerimientos de dicha transformación mientras minimizan el consumo de energía y maximizan la rentabilidad de la planta. En este sentido, los controladores basados en optimización son adecuados para el diseño de sistemas de control que minimicen el consumo de energía de dichos sistemas mientras mantienen su productividad teniendo en cuenta los factores que los afectan. Primero, se presentan como las técnicas de control basadas en optimización pueden contribuir a hacer frente a los desafíos impuestos por la industria de manufactura. Con base en esta revisión, la industria manufacturera se clasifica por niveles, nivel de máquina, línea de proceso, y planta, para el diseño de controladores basados en optimización. Además, para diseñar estrategias de control que no afecten la productividad de la planta, se propone una clasificación para estos sistemas en función de las operaciones realizadas. Con base en estas clasificaciones, se diseñan estrategias de control que minimicen el consumo de energía de los sistemas de manufactura o los costos asociados a dicho consumo. A los niveles de maquina y línea, se diseñaron estrategias de control para minimizar el consumo de energía de los sistemas de manufactura con base en el enfoque de control predictivo basado en modelo. Las estrategias propuestas se basan en la gestión independiente de aquellos dispositivos que no están directamente relacionados con las operaciones de mecanizado. Por lo tanto, modelos de consumo de energía fueron necesarios para predecir el perfil del consumo de energía de estos sistemas y, a partir de esto, seleccionar los instantes de activación/desactivación de los dispositivos manipulados que minimicen el consumo de energía y garanticen el correcto funcionamiento de dichos sistemas. Dado que al nivel de línea el tamaño y la complejidad de estos sistemas aumenta, se propone a una estrategia de control basada en dos modos de control para reducir la carga computacional mediante la conmutación de un modo de control basado en optimización a un modo autónomo que no requiere optimización. Dada la necesidad de sistemas de manufactura flexibles y reconfigurables, estrategias de control no centralizadas se proponen para minimizar el consumo de dichos sistemas a los niveles más altos. Para este fin, los sistemas de manufactura se dividieron en subsistemas, y se diseñaron controladores locales de tipo cooperativo y no cooperativo usando métodos alternativos de dirección de multiplicadores para resolver los problemas de optimización. Además, debido a la naturaleza de los objetivos de control propuesto, se propuso una forma de establecer el consenso entre los controladores locales con dinámicas acopladas. Finalmente, a nivel de planta, se diseñan estrategias de control con base en el enfoque control predictivo basado en modelo económico para maximizar la rentabilidad de la planta. A este nivel, los objetivos de control se centran en determinar la programación de la producción óptima que deberán seguir las estrategias de control diseñadas a niveles más bajos. Así, la programación de la producción de la planta se determina teniendo en cuenta la demanda de piezas, el consumo de energía total, y el mercado energético con sus fluctuaciones. Las estrategias de control propuestas en esta tesis se probaron en simulación considerando diferentes escenarios diseñados con base en la operación real de una planta de fabricación de piezas automotrices.
Aquesta tesi es centra principalment en l’estudi de les tècniques de control basades en optimització per al disseny d’estratègies que contribueixin a millorar l’eficiència energètica dels sistemes de manufactura intel·ligents. Actualment, la indústria manufacturera està experimentant una transformació cap a sistemes de manufactura intel·ligents, flexibles i eficients energèticament, impulsada pels avenços en dispositius de mesura, gestió de dades i eines de comunicació i connectivitat. Aquesta transformació requereix que els sistemes de manufactura siguin modulars i reconfigurables per poder respondre als canvis en la programació de la producció i de la demanda i disseny de les peces mentre continuen operant de manera eficient i sostenible. Per tant, per tal d’assolir una indústria de manufactura m’és intel·ligent, s’han de dissenyar sistemes de control adequats que permetin complir els requeriments d’aquesta transformació, així com també minimitzar el consum d’energia i maximitzar la rendibilitat de la planta. En aquest sentit, els controladors basats en optimització i les arquitectures de control no centralitzat podrien ser adequats per al disseny de sistemes de control que contribueixin a minimitzar el consum d’energia total d’aquests sistemes mentre mantenen la seva productivitat i tenen en compte les restriccions operatives i els factors externs que afecten aquests sistemes. Per tant, mitjançant l’ús d’estratègies de control avançat, els sistemes de control poden ser degudament actualitzats per incloure la informació sobre els canvis en l’operació dels sistemes de manufactura, així com també la variació del mercat energètic per minimitzar els costos d’energia durant l’operació de la planta. Primer, en aquesta tesi, es presenten i discuteixen les estratègies actualment implementades en la indústria manufacturera per millorar la seva eficiència energètica. En base a aquesta revisió, s’identifiquen les principals bretxes de recerca en aquest camp i es discuteix com les tècniques de control basades en optimització poden contribuir a fer front als desafiaments imposats per la nova era de la indústria manufacturera (Industry 4.0). Recolzant-se en la revisió de la literatura, es proposa classificar la indústria manufacturera per nivells, considerant el nivell de màquina, línia de procés i planta, per al disseny de controladors basats en optimització. A més, per tal de dissenyar estratègies de control que no afectin la productivitat de la planta, és a dir, el nombre de peces processades per unitat de temps, els elements constitutius dels sistemes de manufactura també es classifiquen en dispositius de mecanitzat i perifèrics en funció de les operacions realitzades. Els elements de la primera classe corresponen a aquells que estan directament involucrats en les operacions de mecanitzat, mentre que els de la segona classe són aquells que s’encarreguen de proveir els recursos requerits pels dispositius de mecanitzat. Després, en base a aquesta classificació, es proposen estratègies de control en cada nivell per minimitzar el seu consum d’energia o els costos associats a aquest consum. Per als nivells de màquina i línia de procés, es dissenyen estratègies de control per minimitzar el consum d’energia dels sistemes de manufactura en base a l’enfocament de control predictiu basat en model. Les estratègies proposades es basen en la idea de gestionar de manera independent els dispositius (o sistemes) perifèrics per tal de no afectar el temps de processament de les màquines tot mantenint l’operació dels dispositius de mecanitzat. Per tant, calen models de consum d’energia per a predir el perfil de consum d’energia dels sistemes de manufactura i, en base a aquesta predicció, seleccionar els instants d’activació / desactivació per als dispositius manipulats a partir dels quals es minimitzi el consum d’energia total i es pugui garantir el correcte funcionament d’aquests sistemes. D’altra banda, atès que al nivell de línia de procés la mida i la complexitat dels sistemes de manufactura augmenta, es proposa una estratègia de control basada en dos modes de control per tal de reduir la càrrega computacional i dissenyar controladors que puguin ser implementats en temps real. En aquest sentit, tenint en compte que els sistemes de manufactura presenten un comportament diari, es proposa un algoritme per detectar la periodicitat d’aquests sistemes i, després, commutar a un mode de control autònom que no requereixi resoldre un problema d’optimització en línia. D’altra banda, donada la necessitat de sistemes de manufactura flexibles i reconfigurables, es proposen estratègies de control no centralitzades per minimitzar el consum d’energia dels sistemes de fabricació als nivells més alts. Amb aquesta finalitat, els sistemes de manufactura es divideixen en subsistemes, i es dissenyen controladors locals de tipus cooperatiu i no cooperatiu utilitzant mètodes alternatius de direcció de multiplicadors per resoldre els problemes d’optimització de manera distribuïda. A més, a causa de la naturalesa de l’objectiu de control proposat, el qual està enfocat en minimitzar el consum d’energia dels sistemes de manufactura, es proposa una forma d’establir el consens entre els controladors locals amb dinàmiques acoblades. Després, les estratègies de control proposades són extrapolades al nivell de planta usant objectius de tipus econòmic, i es comparen les arquitectures de control centralitzat i no centralitzat pel que fa al seu acompliment en llac¸ tancat i la càrrega computacional requerida per trobar una solució. Finalment, a nivell de planta, es dissenyen estratègies de control en base a l’enfocament de control predictiu basat en model econòmic per tal de maximitzar la rendibilitat de la planta i minimitzar els costos associats a la seva operació. Per tant, a aquest nivell, els objectius de control se centren a determinar la programació de la producció òptima de la planta que hauran de seguir les estratègies de control dissenyades als nivells més baixos. En aquest sentit, la programació de la producció de la planta és determinada tenint en compte la demanda actual de peces, el consum d’energia dels sistemes de manufactura i el mercat energètic amb les seves fluctuacions. Totes les estratègies de control proposades en aquesta tesi es proven en simulació considerant diferents escenaris basats en l’operació real d’una planta de fabricació de peces automotrius.

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-122).
The manufacturing industry has, recently, been facing tremendous challenges, including cost efficiency, system safety, and process automation, and manufacturing companies are required to adopt new technologies to keep themselves sustainable in the fast-changing world of technology. This research focuses, in particular, on how to prevent cutting tool failures and catastrophic accidents in Computerized Numerically Controlled (CNC) machining processes by using a predictive model based on the cutting sound data. With advances in machine learning algorithms and predictive analytics techniques, it becomes possible to create a noise-robust predictive model from an unstructured dataset of sound data. It is an obviously desirable decision to make use of every technology as required and benefit from it. The predictive model introduced in this research uses cutting sound data rather than acoustic emission or force/torque sensor data, which have been widely used for machine failure detection but have shown some limitations. The model is an important stepping stone for realizing an unmanned and fully automated manufacturing system, the so-called "smart factory," and it would be a meaningful movement for the government side as well, taking into account government's responsibility to keep people safe in the workplace. In this research, several experiments were carried out to collect sound data in the CNC machining center in Korea, and particular features were extracted from the analog waveform signals, using the unstructured data to make the predictive model using various advanced data analytics techniques and cutting-edge machine learning algorithms. Then, several analysis methods with systems thinking were used to explore potential impacts of the predictive model on the manufacturing system because the systems thinking approach is the most effective way to analyze a wide range of potential impacts from a holistic perspective. Specifically, the impact analysis was successfully conducted by using a "Causal Analysis based on STAMP (CAST)," which is a system safety analysis method. Also used was "system dynamics modeling," which is generally employed to identify dynamic behaviors in a complex system. Finally, a "complete value template" was constructed to portray how the new system delivers value to its stakeholders from a system architecture perspective.
by Hyunsoo Jeong.
S.M. in Engineering and Management

The recent development of smart connected devices, in this paper defined as cyberphysical systems (CPS), within the context of Industry 4.0 presents an opportunity for the manufacturing industry to reach new levels of efficiency. The introduction of novel technologies onto the factory floor will however affect the manufacturing execution systems (MES) currently deployed. There exists an uncertainty whether the existing MES are able to be integrated with these new technologies. This paper aims to identify and investigate the main areas affected by the integration of CPS and MES. The results, gained through scientific studies as well as interviews with segments of concerned industry and researchers, allowed us to identify six areas that will be affected. Out of the six areas, two have evolved as to be of most interest, namely architectural integration of systems and the human resource area, since change within the other four areas depend on them. We found that an integration of CPS and MES will not be possible unless two key factors are considered, a standardization of communication between systems and a knowledgeable, open minded and inter-communicating workforce.
Den senaste tidens utveckling av smarta uppkopplade enheter, i texten benämnda cyberphysical systems, inom vad som kallas Industri 4.0 medför en möjlighet för effektivisering inom tillverkningsindustrin. Introduktionen av ny teknik inom tillverkningsindustrin kommer dock att påverka de befintliga produktionsstyrningssystemen och det råder idag en osäkerhet kring om en integration är möjlig. I denna uppsats är målet att identifiera och undersöka de områden som påverkas när smarta uppkopplade enheter introduceras i fabriker. Genom litteraturstudier, såväl som intervjuer med aktörer inom berörd industri och forskare, har vi identifierat sex påverkade områden. Utav dessa sex områden anser vi att två är utav störst intresse. Detta då utvecklingen inom dessa två områden kan ses som en förutsättning för utveckling inom de övriga områdena. De två områdena är systemintegration och personalresurser. Vi fann att en integration mellan smarta uppkopplade enheter och produktionsstyrningssystem endast är möjlig om två nyckelfaktorer beaktas, en standardisering av kommunikation mellan system och en välutbildad, öppen, arbetsstyrka.

With the advent of the fourth industrial revolution, information technology and manufacturing systems are merging to form what is now known as Smart Manufacturing. However, with this newer technology being integrated with newer pieces of machining equipment, companies with legacy equipment occasionally are in a bind since these machines were not designed or built with the fundamental components of smart manufacturing systems: unified connectivity, real-time monitoring, and issue identification. The purpose of this research is to provide a solution for converting manual machining equipment into smart systems with these fundamental components of smart manufacturing. The pieces of equipment that were the subjects of this experimentation were an HJ-1100 Kingston lathe and four ACER Vertical Turret Milling machines. None of these machines had any of these capabilities at the inception of this project.These machines were successfully converted into smart systems with varying degrees of reliability between the lathe and the four mills in the case of real-time monitoring and issue identification. The setups and configurations to achieve these three smart components are described and provided.

Background: Service industries have grown extensively over the past few decades on the back of globalized business trends. With increasing competition, product firms are struggling on product sales alone. Hence, both products and services are being bundled into what is known as offerings. Moreover, firms are looking into how they can improve their offerings to meet customer needs with the help of smart products. Smart products are described as products able to communicate and interact with other electronic devices as well as being self aware. One of these examples is conditional monitoring whereby the product is houses built in sensors to communicate with a back end ERP system providing the supplier a transparent view and real-time update into the status and service needs for both the product and customer. Purpose: The aim of this thesis is to explore how smart products can help leverage services for product firms moving towards a service focus. Method: In addressing the purpose a case study strategy was applied. An inductive approach was used, and interviews were conducted with two Swedish manufacturers, SKF and Atlas Copco. SAP, a software provider was also interviewed. Lastly, a qualitative approach was used and secondary data was collected through annual reports, as well as public company information. Conclusions: Smart products show the capability of being able to record, transmit and act upon their behavior and usage. One major finding from the thesis is that smart products enable product firms to extend their service portfolios from a transactional to a relational standpoint through real time information feeds. This includes asset maintenance as well as monitoring and visibility into client operations. In addition, traditional product firms help product firm’s move towards a service strategy. Another finding of the thesis is that information visibility shows a positive co-relation with the service provider’s ability to take on more risk increasing service revenues and customer lock in and increase value co-creation. On the other hand smart products show to be challenging to product firms new to service development. These challenges include increasing initial infrastructure costs and high level of maintenance and complexity of the smart products.

This thesis set out to investigate what motivates an established manufacturing firm to add smart and connectivity features into its products and what considerations are made when doing so. Such a firm was used as a case study object, a firm that is considering digitalization in one of its product areas but has not yet finalized the strategy on how to move forward. The qualitative study was made using semi-structured interviews which included mostly respondents from this organization, but also respondents representing the customers as small company entrepreneurs.  A literature study was conducted to better define the subject of product digitalization. Previous research on smart and connected products as well as related business and market theories were identified as relevant, as well as literature on digital transformation. Based on the literature study, a theoretical framework was formulated in the form of an iterative model on product digitalization, where the business and market mechanics are included. A qualitative case study was then conducted in the form of semi-structured interviews, with topics considered relevant based on the literature study. The empirical data shows many similarities compared to the current literature on the topic. The literature study has identified these aspects as relevant to be considered by an established manufacturing firm and compared them with the case study firm: ·       Digital vision – with clear intent and outcome for both the business and the customer ·       Customer intimacy – services related to smart and connected products will increase the intimacy to the end customer ·       Business model – product sales business model will be transformed by servitization ·       Technology content – smart and connected products need integration of new technologies, such as hardware, software and data management ·       Capabilities – necessary capabilities and the ability to integrate and coordinate these ·       Competitive strategy – using digital technology to gain competitive advantage.  ·       Technology acceptance –what technologies are accepted by the customers and why The case study shows that the firm have identified several key areas affected by product digitalization. Many of these aspects are considered when making strategic decisions, and the members of the organization are including most of them when building scenarios. One area of concern is the ability to integrate and coordinate all necessary functions, both for technology and services. Another is the switch in mindset going from a predictable product business model to a innovate digital one. Offering services will bring them into more direct contact with the customers, and possibly change the role of the dealership network.

Purpose: The purpose of this paper is to identify the opportunities that Industry 4.0 brings within the framework of applying Cyber Physical Systems in an environment of a Smart Factory. This paper shall identify the changes within daily business processes and the impact of these changes on the daily business life. Design/Methodology/Approach: The research is carried out as a case study research. Due to a qualitative approach for this case study interviews are conducted and the results are analyzed and discussed. Findings: Industry 4.0 will change the way we are working today and influence businesses and business processes in many ways. Data handling, processes and efficiency will change and the way we perceive manufacturing will change in a long term view. Further Research: It would be recommended to expand this research by conducting more research in this particular field as well as impacts on the employee should be studied more in detail.

Idag pratas det mycket om smart teknik och man säger att den fjärde industriella revolutionen är på väg. Revolutionen kallas för Industri 4.0 och innebär två tekniska förbättringar, Internet of Things (IoT) och Cyber-Physical Systems (CPS). IoT låter fysiska enheter sammankopplas i ett system med andra enheter med hjälp av elektromagnetiska vågor och CPS ger möjligheten till att få in information från omvärlden och implementera informationen i digital form. När det kommer till implementering i tillverkningsindustrin används begreppen Industrial Internet of Things och Cyber-Physical Production Systems. Arbetet består av en djupgående litteraturstudie och undersöker vad implementering av IoT och CPS i personbilsbranschens tillverkningssystem kan leda till och hur de fungerar i praktiken. Teorin utgår från vetenskapliga artiklar, tidskrifter och journaler samt en studie från Atlas Copco. Eftersom att smart teknik är ett brett ämne och vi behövde förhålla oss till en tidsgräns på 18 veckor avgränsades arbetet till endast IoT och CPS i tillverkande personbilsföretag. Branschen för personbilar valdes för att i jämförelse med andra branscher är både kvaliteten och kvantiteten avgörande. Samtidigt som det produceras många personbilar måste varje personbil uppfylla en rad olika krav och varje enhet utgör en betydande del av kapitalet i företaget.Resultatet visar hur IoT och CPS fungerar som helhet och vad för positiva och negativa konsekvenser implementering av begreppen ger. Av resultatet framgår också att faktorerna produktion, ekonomi och människa ska analyseras som en helhet och inte enskilt för att implementeringen ska vara framgångsrik i tillverkande personbilsföretag.Möjligheterna som IoT och CPS medför är snabbare och exaktare beslut, systemövervakning och insamling, utbyte och analysering av data för personbilsbranschens företag. Den största utmaningen som implementeringen av begreppen medför är datahantering. Det finns en risk att oönskade mottagare får tillgång till konfidentiell information genom bland annat dataläckage och dataintrång. Således bör fokus ligga på att förebygga detta för att få ut fördelarna och samtidigt reducera nackdelarna. Slutsatsen som kan dras av resultatet är att en kombination av IoT och CPS i personbils-branschens tillverkningssystem skapar ett kommunikationsnätverk bland heterogena enheter som gör att system kan kommunicera och utbyta data med varandra på ett effektivt sätt.Implementering av begreppen leder till minskning av defekter, introduktionskostnader, energianvändning och upplärning för arbetare samt ökad verktygsdrift och produktivitet.
Today, there is much talk about smart technology and it is said that the fourth industrial revolution is on its way. The revolution is called Industry 4.0 and involves two technical improvements, the Internet of Things (IoT) and Cyber-Physical Systems (CPS). IoT allows physical devices to be interconnected in a system with other devices using electromagnetic waves and CPS provides the opportunity to get information from the outside world and implement the information in digital form. When it comes to implementation in the manufacturing industry, the concepts Industrial Internet of Things and Cyber-Physical Production Systems are used.The thesis consists of an in-depth literature study and investigates what implementation of IoT and CPS in the automotive industry's manufacturing system can lead to and how they work in practice. The theory is based on scientific articles, paper and journals, and a study by Atlas Copco. Because smart technology is a broad topic and we needed to relate to a time limit of 18 weeks, the work was limited to IoT and CPS only in manufacturing passenger car companies. The industry for passenger cars was chosen so that, in comparison with other industries, both the quality and the quantity are decisive. While many passenger cars are being produced, each passenger car must meet a variety of requirements and each unit constitutes a significant part of the capital of the company.The result shows how IoT and CPS work as a whole and what positive and negative consequences the implementation of the concepts gives. The result also shows that the factors of production, economy and humanity should be analysed as a whole and not individually in order for the implementation to be successful in manufacturing passenger car companies.The opportunities that IoT and CPS entail are faster and more precise decisions, system monitoring and collection, exchange and analysis of data for the automotive industry's companies. The biggest challenge that the implementation of the concepts entails is data management. There is a risk that unwanted recipients will have access to confidential information through, among other things, data leakage and hacking. Thus, the focus should be on preventing this in order to get the benefits and at the same time reduce the disadvantages.The conclusion that can be drawn from the result is that IoT and CPS in the automotive industry's manufacturing system create a communication network among heterogeneous units that enable systems to communicate and exchange data with each other in an efficient manner. Implementation of the concepts leads to a reduction of defects, introduction costs, energy use and training for workers, as well as increased tool operation and productivity.

Dans l'industrie 4.0, les machines des usines sont équipées de capteurs qui collectent des données pour une surveillance efficace de l'état des équipements. C’est une tâche difficile car elle nécessite l’intégration et le traitement de données hétérogènes provenant de différentes sources, avec des résolutions temporelles et des significations sous-jacentes différentes. Les ontologies apparaissent comme une méthode pertinente pour traiter l’intégration des données et pour représenter la connaissance de manière interprétable par les machines grâce à la construction de modèles sémantiques. De plus, la surveillance des processus industriels dépend du contexte dynamique de leur exécution. Dans ces circonstances, le modèle sémantique lui-même doit évoluer afin de représenter dans quelle(s) situation(s) se trouve(nt) la ou les ressources pendant l’exécution de ses tâches pour soutenir la prise de décision. Cette thèse étudie l’utilisation des méthodes de représentation des connaissances pour construire un modèle sémantique évolutif qui représente le domaine industriel, en mettant l’accent sur la modélisation du contexte pour fournir la notion de situation
In Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must evolve in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This thesis studies the use of knowledge representation methods to build an evolving semantic model that represents the industrial domain, with an emphasis on context modeling to provide the notion of situation

Den globala industrin har under det senaste decenniet genomgått en enorm digital transformation, där tillämpandet av digitala och smarta verktyg inom företag aldrig har varit mer påtagligt. Under november 2011 presenterades begreppet Industrial 4.0 i en artikel skriven av den Tyska regeringen som beskriver en teknikintensiv strategi för år 2020 och omfattar vad idag betraktas som den fjärde industriella revolutionen. Industri 4.0 utgörs till stor del av integrationsprocessen mellan teknik och övrig verksamhet inom ett tillverkningsföretag, vilket i sin tur ger upphov till teknik såsom; automation, förstärkt verklighet, simuleringar, intelligenta tillverkningsprocesser samt övriga processindustriella IT-verktyg och -system. Flertal forskningsstudier hävdar att Industri 4.0-teknologier har potential att revolutionera sättet företag idag tillverkar produkter, men i och med att begreppet är relativt nytt, abstrakt samt består av väldigt komplexa tekniker och komponenter, är införandet av dessa inom en tillverkningsmiljö för närvarande en stor utmaning för tillverkande företag. Denna studie syftar alltså till att belysa de utmaningar och hinder som större tillverkande företag möter vid implementering av digital och smart teknik, samt åtgärder för att motverka dessa. Målet med studien är att leverera ett användbart resultat både för aktiva företag inom tillverkningsindustrin i form av stöd vid analys och diskussion av eventuella implementeringsstrategier och -satsningar inom Industri 4.0, men också ge övriga intressenter en uppfattning kring ämnet med tanke på att det, som sagt, är ett abstrakt system. En litteraturstudie genomfördes både för att få en överblick kring ämnet Industri 4.0 och hur det har behandlats i tidigare examensarbeten, avhandlingar samt forskningsstudier, men även för att identifiera tidigare identifierade hinder. Därefter genomfördes fältstudier på två tillverkande företag, Scania och Atlas Copco, samt teknikkonsultföretaget Knightec. Syftet med detta var framförallt att få en mer påtaglig och verklighetsförankrad uppfattning av Industri 4.0 men även verifiera att informationen i den teoretiska delen är relevant i praktiken för en tillverkande verksamhet. Studien påvisade därtill att identifierade utmaningar och hinder återfinns bland flertal organisatoriska områden inom ett tillverkande företag, varav de mest framgående aspekterna omfattade strategi, ledarskap, kunder, kultur, anställda, juridik samt teknik. Resultatet avslöjade vidare att tillverkande företag präglas av bristfälliga planer och strategier för att identifiera samt implementera nya tekniska lösningar, konflikter bland de anställda, svårigheter att integrera kundsystem enhetligt inom produktionen, avsaknad av lämplig teknisk kompetens, juridiska problem vad gäller hantering av data samt svårigheter att integrera nya och gamla teknologier.
The global industry has during the last decade undergone a considerable digital transformation, whereas the application of digital and smart technology within companies has never been more of a relevant field. During November of 2011, the term Industrial 4.0 was presented in an article written by the German government to describe a technology intensive strategy for the year 2020 and signifies what today is defined as the fourth industrial revolution. Industry 4.0 largely consists of the integration process between technology and remaining operations within a manufacturing company, which enables the development of technologies such as; automation, augmented reality, simulations, intelligent manufacturing processes and other process industrial IT-tools and systems. Several research studies has suggested that Industry 4.0 technologies has the potential to revolutionize the way companies today manufacture products, however, since the concept is relatively new, abstract and consists of various complex technologies and components, the implementation process of these within a manufacturing environment is one largest challenges that manufacturing companies are facing. This study therefore aims to highlight the challenges and difficulties that large manufacturing companies are facing when implementing digital and smart technology, as well as provide solutions regarding how they can be overcome. The overall goal is to deliver useful results both for active companies within the manufacturing industry in regards to serving as support when analyzing and discussing possible implementation strategies as well investments related to Industry 4.0, but also to provide surrounding stakeholders with a perception of the subject. At the commencement of the project, a literature study was performed to develop an overview of how Industry 4.0 has been discussed in previous theses and research studies as well as to find previously identified difficulties regarding the implementation process. Finally, a field study was performed at Scania and Atlas Copco and at the technology consulting firm Knightec. The main purpose was to gain a more realistic perspective regarding how digitalization and Industry 4.0 systems are applied and to verify that the information from our theoretical study is relevant and applicable within an actual manufacturing company. The study furthermore revealed that the identified difficulties and challenges can be found within multiple organizational areas of a manufacturing company, whereas the most distinct aspects consisted of strategy, leadership, customers, culture, employees, legal governance as well as technology. The results showed that companies were characterized by an overall lack of strategy to implement new technologies, conflicts with employees during implementation, difficulties to integrate customer orders with production, lack of technical skills in staff, legal issues regarding data storage and difficulties integrating new and old technologies.

The term Industry 4.0, sometimes referred to as a buzzword, is today on everyone’s tongue and the benefits undeniably seem to be promising and have potential to revolutionize the manufacturing industry. But what does it really mean? From a high-level business perspective, the concept of Industry 4.0 most often demonstrates operational efficiency and promising business models but studies show that many companies either lack understanding for the concept and how it should be implemented or are dissatisfied with progress of already implemented solutions. Further, there is a perception that it is difficult to implement the concept without interference with the current production system.The purpose of this study is to interpret and outline the main characteristics and key components of the concept Industry 4.0 and further break down and conclude the potential benefits and enablers for a manufacturing company within the heavy automotive industry. In order to succeed, a case study has been performed at a manual final assembly production unit within the heavy automotive industry. Accordingly, the study intends to give a deeper understanding of the concept and specifically how manual assembly within an already existing manual production system will be affected. Thus outline the crucial enablers in order to successfully implement the concept of Industry 4.0 and be prepared to adapt to the future challenges of the industry. The case study, performed through observations and interviews, attacks the issue from two perspectives; current state and desired state. A theoretical framework is then used as a basis for analysis of the result in order to be able to further present the findings and conclusion of the study. Lastly, two proof of concept are performed to exemplify and support the findings. The study shows that succeeding with implementation of Industry 4.0 is not only about the related technology itself. Equally important parts to be considered and understood are the integration into the existing production system and design and purpose of the manual assembly process. Lastly the study shows that creating understanding and commitment in the organization by strategy, leadership, culture and competence is of greatest importance to succeed.
Begreppet Industri 4.0, ibland benämnt som modeord, är idag på allas tungor och fördelarna verkar onekligen lovande och tros ha potential att revolutionera tillverkningsindustrin. Men vad betyder det egentligen? Ur ett affärsperspektiv påvisar begreppet Industri 4.0 oftast ökad operativ effektivitet och lovande affärsmodeller men flera studier visar att många företag antingen saknar förståelse för konceptet och hur det ska implementeras eller är missnöjda med framstegen med redan implementerade lösningar. Vidare finns det en uppfattning att det är svårt att implementera konceptet utan störningar i det nuvarande produktionssystemet. Syftet med denna studie är att tolka och beskriva huvudegenskaperna och nyckelkomponenterna i konceptet Industri 4.0 och ytterligare bryta ner och konkludera de potentiella fördelarna och möjliggörarna för ett tillverkande företag inom den tunga bilindustrin. För att lyckas har en fallstudie utförts vid en manuell slutmonteringsenhet inom den tunga lastbilsindustrin. Studien avser på så sätt att ge en djupare förståelse för konceptet och specifikt hur manuell montering inom ett redan existerande manuellt produktionssystem kommer att påverkas. Alltså att kartlägga viktiga möjliggörare för att framgångsrikt kunna implementera konceptet Industri 4.0 och på så sätt vara beredd att ta sig an industrins framtida utmaningar. Fallstudien, utförd genom observationer och intervjuer, angriper frågan från två perspektiv; nuläge och önskat läge. Ett teoretiskt ramverk används sedan som underlag för analys av resultatet för att vidare kunna presentera rön och slutsats från studien. Slutligen utförs två experiment för att exemplifiera och stödja resultatet. Studien visar att en framgångsrik implementering av Industri 4.0 troligtvis inte bara handlar om den relaterade tekniken i sig. Lika viktiga delar som ska beaktas och förstås är integrationen i det befintliga produktionssystemet och utformningen och syftet med den manuella monteringsprocessen. Slutligen visar studien att det är av största vikt att skapa förståelse och engagemang i organisationen genom strategi, ledarskap, kultur och kompetens.

La technologie sans fil est un catalyseur clé des promesses de l’industrie 4.0 (fabrication intelligente). En tant que telle, la technologie sans fil sera adoptée comme mode de communication principal au sein de l’usine en général et dans les unités de production en particulier. La communication des unités de production en usine a des exigences particulières en matière de latence, de fiabilité, d’échelle et de sécurité qui doivent d’abord être satisfaites par la technologie de communication sans fil utilisée. Le sans fil est considéré comme une forme de communication non idéale dans la mesure où par rapport aux communications câblées, il est considéré comme moins fiable (avec perte) et moins sécurisé. Ces dégradations possibles entraînent un retard et une perte de données dans un système d’automatisation industrielle où le déterminisme, la sécurité et la sûreté sont considérés comme primordiaux. Cette thèse étudie les exigences d’une communication sans fil dans les unités de production et l’applicabilité de la technologie sans fil existante dans ce domaine. Elle présente une modélisation SysML de l’architecture du système et des flux de données. Elle fournit une méthode d’utilisation des bases de données de type graphe pour l’organisation et l’analyse des données de performance collectées à partir d’un environnement de test. Enfin, la thèse décrit une approche utilisant l’apprentissage automatique pour l’évaluation des performances d’un système d’objets connectés dans le domaine de fabrication
Wireless technology is a key enabler of the promises of Industry 4.0 (Smart Manufacturing). As such, wireless technology will be adopted as a principal mode of communication within the factory beginning with the factory enterprise and eventually being adopted for use within the factory workcell. Factory workcell communication has particular requirements on latency, reliability, scale, and security that must first be met by the wireless communication technology used. Wireless is considered a non-ideal form of communication in that when compared to its wired counterparts, it is considered less reliable (lossy) and less secure. These possible impairments lead to delay and loss of data in industrial automation system where determinism, security, and safety is considered paramount. This thesis investigates the wireless requirements of the factory workcell and applicability of existing wireless technology, it presents a modeling approach to discovery of architecture and data flows using SysML, it provides a method for the use of graph databases to the organization and analysis of performance data collected from a testbed environment, and finally provides an approach to using machine learning in the evaluation of cyberphysical system performance

Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiques
In the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets

This thesis proposes a new medium access control protocol which lies on top of LoRa at 2,4 GHz. Its name is LoRaIN and, in fact, it is intended for a monitoring Industrial Internet of Things application. First, this document provides a general description of the network architecture and relative functionalities. In particular, the centralized nature of the protocol is described in detail, being a discriminating factor with the others IIoT layer 2 protocols. A fundamental feature of LoRaIN is that the devices are not battery driven and, conversely, they are charged via Wireless Power Transfer technology. In fact, an important emphasis is given to the time division between nodes charges and LoRa communications. Finally, some numerical computations have been performed to show the network performance; the latter have been taken as reference for the hands-on work.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Manufacturing Program at MIT, May, 2020
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, in conjunction with the Leaders for Manufacturing Program at MIT, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 63-65).
The objective of this project is to demonstrate the technical ability and cost-effectiveness of reducing electric transmission system overvoltage violations using distributed generation (DG) smart inverters connected to the electric distribution system. Overvoltage violations are situations when the system exhibits voltage levels outside of the acceptable range set by the American National Standards Institute (ANSI) of 105% of nominal system voltage. The challenge that Atlantic Electric could potentially face from the rapid deployment of DG across its distribution system - driven by new additional renewable energy incentive programs in the US State in which it operates - is the underloading of its high voltage (69kV and 115kV) transmission lines causing overvoltage violations at the ends of the transmission lines. The traditional response to this challenge is to install system upgrades on the transmission system in the form of shunt reactors.
However, these system upgrades are expensive and time-consuming to install, which could de-incentivize and delay the deployment of DG projects. The solution we propose is to utilize the reactive power absorption capability of the DG inverters to absorb excessive reactive power from the transmission system. In this work, we investigate feeders' maximum capability of reactive power absorption through distributed generation (DG) smart inverters by modeling two "representative" Atlantic Electric distribution feeders under different PV deployment scenarios based on the feeders' load and generation levels, among other factors. We then perform a cost-benefit analysis to compare against installing shunt reactors. Our findings show that implementing an inverter-based solution has a range of significant cost-savings of up to $300,000/year when compared with installing shunt reactors on the transmission system.
This arrangement, however, is one that hinges on the utility's ability to review regulatory and commercial with all stakeholders involved.
by Ali Said Alrayes.
M.B.A.
S.M.
M.B.A. Massachusetts Institute of Technology, Sloan School of Management
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

With the increasing popularity of blockchain - the cryptocurrency technology, the decentralized potential of the Blockchain technique is driving a new wave across the manufacturing industry. This paper introduce how to use the blockchain technique as a tool for solving supply chain related tasks in manufacture industry, and drive quantum leaps in efficiency, agility and innovation comparing with traditional centralized management system. This paper introduces the blockchain technique with its value properties and the requirement of this technique from manufacture industry. It also presents a clear blockchain architecture based on manufacture industry supply chain management mechanism describing its characteristics, unique consensus algorithms, smart contracts, network, scalability, databases. The paper also gives out a practical supply chain Dapp upon this architecture.
I och med det ökande intresset för kryptovaluta-teknologin Blockchain, går decentraliseringen av Blockchain-tekniken som en ny våg över tillverkningsindustrin. Denna uppsats syftar till att introducera hur tekniken av blockchain kan användas som ett verktyg för att lösa problem relaterade till leverantörskedjan i tillverkningen. Den belyser även vilka fördelar tekniken har gällande effektivitet, flexibilitet och förnyelse jämfört med traditionella centraliserade styrningssystem. Arbetet presenterar fördelarna med blockchain och hur industrin är i behov av denna teknik. Uppsatsen presenterar även en tydlig blockchain konstruerad struktur baserad på tillverkningskedjans mekanism som består av unika algoritmer, nätverk och databaser. Ett praktiskt exempel på en decentraliserad applikation baserat på denna struktur ges även.

In this new digital age, efficiency, quality and competition are all increasing rapidly as companies leverage the Industrial Internet of Things (IIoT). However, while industrial innovation moves at a faster and faster pace, educational institutions have lagged in the development of the curriculum and environment needed to support further development of the IIoT. To fully realize the potential of the IIoT in the manufacturing sector educational institutions must support the technological training and education rigor demanded to instill the skills and thought leadership to move the industry forward. The purpose of this research is to provide an IIoT core system in an educational factory environment. This system will assist in teaching basic principles of IIoT in the factory while simultaneously allowing for students to envision the manufacturing journey of any facility by implementing principles of IIoT. This will be accomplished by providing all the following capabilities together in a single data system: unified connectivity, role-based data display, real-time issue identification, data analytics, and augmented reality.

Small and medium-sized enterprises are highly limited on resources for the transformation into smart factories. Nytt AB, a new startup specialized in smart manufacturing solutions, is completely focused on taking down the barriers with a basic solution: implementing a machine vision system with the purpose to monitor the machines of the factories. The main aim of this thesis is to analyze the data collected from two different machines of a medium-sized factory by monitoring the color states of the stack lights.First of all, some topics are analyzed in order to get a better understanding and knowledge of the main topic of this thesis: smart manufacturing. Secondly, the methodology used during the project is explained. Thirdly, the product developed by Nytt AB is described to get a better understanding. Together with this, the companies where the product is implemented are described. The next step is the presentation of the results by analyzing the data according to these parameters:(i), the availability of the machines, (ii), critical machine tool analysis; (iii),machine idling time; (iv), disruption events; and finally, (v), information transfer. In the results, some graphs and discussions are presented. In the following chapter the conclusions are presented, which allow the analyzed company to improve its current state. Lastly, the relocation of the product into the critical machine, the implementation of new sensors to detect temperature and vibration values of the machines and the implementation of the module OpApp within the factories are suggestions presented as future work at the end of this report.
Små och medelstora företag har mycket begränsade resurser för omvandling till smarta fabriker. Nytt AB, ett nystartat företag inom smart tillverkning, är helt fokuserad på att ta bort hinder med en enkel lösning: implementering av ett kamerasystem för övervakning av maskiner i fabriker. Huvudsyftet med detta examensarbete är att analysera data som samlats in från två olika maskiner i en medelstor fabrik genom att övervaka färgändringar i deras ljuspelare. För det första analyseras några ämnesområden för att få en bättre förståelse och kunskap om huvudtemat i detta examensarbete: smart tillverkning. För det andra förklaras den metod som används under projektet. För det tredje beskrivs den produkt som utvecklats av Nytt AB för att få en bättre förståelse. Tillsammans med detta beskrivs de företag där produkten implementeras. Nästa steg är presentationen av resultatet genom att analysera data enligt följande parametrar:(i), maskinens tillgänglighet; (ii), kritisk verktygsmaskinanalys; (iii), maskinens tomgångstid; (iv), störningshändelser och slutligen; (v), informationsöverföring. I resultatet presenteras några grafer och diskussioner. Slutsatserna presenteras därefter. Dessa slutsatser gör att det analyserade företaget kan förbättra sitt nuvarande tillstånd. Som framtida arbete föreslås slutligen flytt av kamerasystemet till den kritiska maskinen, införande av nya sensorer för att övervaka temperaturer och vibrationsvärden för maskinerna och implementeringav modulen OpApp i fabriker.

L’obbiettivo della tesi è tracciare un quadro generale relativo agli ambiti fondamentali di Industry 4.0, quindi capire in cosa consiste, analizzarne l’impatto sul mondo del lavoro, le principali tecnologie abilitanti e infine il piano economico su cui si inseriscono le varie iniziative ad esse correlata. Per questo, fornita una definizione accademica che indica in maniera chiara cosa riguarda Industry 4.0, si procede ad un’analisi dei fattori che di fatto impongono questa rivoluzione, dell’impatto che tali cambiamenti avranno nell’ambito occupazionale, delle competenze lavorative e del ruolo umano all’interno del processo produttivo. Si analizza poi come gli attuali modelli produttivi basati sulla Lean Production si debbano interfacciare con le nuove tecnologie e quindi il rapporto tra LPD e I4.0. L’analisi prosegue illustrando le principali tecnologie abilitanti attraverso una loro definizione ed evidenziando i vantaggi che ne derivano attraverso casi studio pratici. Infine vengono brevemente presentati i piani economici delle principali nazioni con cui l’Italia si confronta sul piano dell’industrial manufacturing: Usa, Germania, Francia, e viene effettuata un’analisi più approfondita del piano nazionale “Industria 4.0” avvalendosi anche di un approfondimento della Legge di bilancio.

The so-called Industry 4.0 is by its agitators commonly denoted as the fourth industrial revolution and promises to turn the manufacturing sector on its head. However, everything that glimmers is not gold and in the backwash of hefty consultant fees questions arises: What are the drivers behind Industry 4.0? Which barriers exists? How does one prepare its manufacturing procedures in anticipation of the (if ever) coming era? What is the internet of things and what file sizes’ is characterised as big data? To answer these questions, this thesis aims to resolve the ambiguity surrounding the definitions of Industry 4.0, as well as clarify the fuzziness of a data-driven manufacturing approach. Ergo, the comprehensive usage of data, including collection and storage, quality control, and analysis. In order to do so, this thesis was carried out as a case study at GKN Aerospace Sweden (GAS). Through interviews and observations, as well as a literature review of the subject, the thesis examined different process’ data-driven needs from a quality management perspective. The findings of this thesis show that the collection of quality data at GAS is mainly concerned with explicitly stated customer requirements. As such, the data available for the examined processes is proven inadequate for multivariate analytics. The transition towards a data-driven state of manufacturing involves a five-stage process wherein data collection through sensors is seen as a key enabler for multivariate analytics and a deepened process knowledge. Together, these efforts form the prerequisites for Industry 4.0. In order to effectively start transition towards Industry 4.0, near-time recommendations for GAS includes: capture all data, with emphasize on process data; improve the accessibility of data; and ultimately taking advantage of advanced analytics. Collectively, these undertakings pave the way for the actual improvements of Industry 4.0, such as digital twins, machine cognition, and process self-optimization. Finally, due to the delimitations of the case study, the findings are but generalized for companies with similar characteristics, i.e. complex processes with low volumes.

The diploma thesis deals with the analysis of business environment of the TOROLA design Ltd. On the basis of established information a concept and options for project execution of production and distribution of the electronic system for „Smart houses“ will be configured. This project will make extending the range of products and services possible the company and it will help to improve its financial situation.

Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.

The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes.

The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors

HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.

碩士
國立成功大學
製造資訊與系統研究所
106
Manufacturing Execution System (MES) is one of the manufacturing systems that integrates real-time production information with other information systems (such as production planning and scheduling systems) to make business, plant or process control systems to be linked, and improve business operations and production performance. In addition, the MES system collects data from people, machines, materials, processes, environmental, and production-related data from the manufacturing process to monitor impact factors on production performance or critical control points for guaranteeing efficiency and quality of production. The emergence of big data, the advancement of information technology and the improvement of computing power have led to the re-emergence of artificial intelligence (AI) and its successful application in many fields, and the industry has entered the era of Industry 4.0. In the industry 4.0 environment, Data Science's methods and technologies are widely used. From the data to extract valuable information to improve decision-making, MES systems must also evolve into Smart-MES to adapt to intelligent production. Demand. This study focuses on the requirement of industrial 4.0 intelligent production, using Data Science's method and artificial intelligence technology, planning and designing Smart MES model and data analysis architecture, and developing its analysis technology of impact factors on production performance and forecasting technology of production. The results of this study will help to realize intelligent production, and thus enhance the competitiveness of the industry.

abstract: Recent advances in manufacturing system, such as advanced embedded sensing, big data analytics and IoT and robotics, are promising a paradigm shift in the manufacturing industry towards smart manufacturing systems. Typically, real-time data is available in many industries, such as automotive, semiconductor, and food production, which can reflect the machine conditions and production system’s operation performance. However, a major research gap still exists in terms of how to utilize these real-time data information to evaluate and predict production system performance and to further facilitate timely decision making and production control on the factory floor. To tackle these challenges, this dissertation takes on an integrated analytical approach by hybridizing data analytics, stochastic modeling and decision making under uncertainty methodology to solve practical manufacturing problems. Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
Dissertation/Thesis
Doctoral Dissertation Industrial Engineering 2020

O setor industrial, sendo um dos grandes impulsionadores do desenvolvimento macroeconómico global, torna-se também um objeto interessante de investimento por parte de entidades públicas e privadas. Face à crescente procura por produtos e serviços que possam suprir as necessidades de toda a população mundial, torna-se necessário a criação em massa de produtos com o mínimo de gasto (zero waste) e o máximo de eficiência. Num chão de fábrica, este objetivo exige soluções que permitam a recolha e a troca de dados dos vários sistemas de informação que apoiam os seus processos de negócio. Estes dados permitem monitorizar as operações, melhorar o planeamento, melhorar os processos e reduzir os erros e defeitos de fabrico. O presente trabalho propõe uma arquitetura de software para a monitorização de um chão de fábrica, baseada no protocolo de comunicação máquina-máquina aberto Open Platform Communications - Unified Architecture (OPC-UA) desenvolvido pela OPC Foundation. Este protocolo posiciona-se para vir a ser um standard de facto da indústria. Os trabalhos iniciaram-se com um levantamento do estado da arte por forma a apurar como a digitalização das empresas fabris pode maximizar os indicadores de produtividade. Com base nos desafios identificados foi realizado um estudo de caso da digitalização de uma empresa de moldes com o desenvolvimento de uma framework que utiliza o protocolo industrial OPC-UA como forma de integração entre um sistema de informação do tipo Enterprise Resource Planning (ERP) e todos os serviços que compõem uma arquitetura de software industrial, permitindo observar em tempo real indicadores de desempenho, bem como digitalizar a gestão de ordens de fabrico num chão de fábrica. O presente projeto insere-se no projeto Mobilizador TOOLING4G – Advanced Tools for Smart Manufacturing (~ 7M€ e 32 parceiros) e enquadra-se no cluster engineering & tooling, que integra uma cadeia de valor alargada (do design ao produto final), para responder a clientes globais que cada vez mais pretendem soluções chave-na-mão.

碩士
元智大學
管理碩士在職專班
107
Taiwan's manufacturing advantages are gradually losing, and it is necessary to introduce smart manufacturing to enhance competitiveness. This study uses collaborative design and intelligent systems to analyze the cooperative design and product integration services of Solen electric company. Research purposes: First, the product development stage, the application of collaborative design with customers, the difference between before and after and the verification situation. Second, the benefits of smart system in the application of mold design, manufacturing and processing. Research questions: First, the application of smart system in collaborative design? How to change the design process at each stage? How does smart system and collaborative design work? Second, how does the mold design and processing use smart system to analyze the results? This study results show that the case company cooperates with the design and manufacturing service model-smart product integration services. According to the intelligent manufacturing system, mold design, smart manufacturing processing, mold design assistance and mold smart design. The processing PowerMILL system and other software enhance the company Mold design capability and processing precision and speed. The four companies in which the case company introduced collaborative intelligence into collaborative design are all Type I. It is known that the case companies are currently cooperative and intensive, demonstrating the degree of collaborative design of the case companies is high. The part of smart system is that manufacturers with high degree of collaborative design have relevant functions, which can continue to promote the advantages of smart manufacturing and increase the customer base of case companies. Finally, management practice recommendations, research limitations and future research proposals are proposed.

Research Doctorate - Doctor of Philosophy (PhD)
The aim of this research is to address issues related to the effective use of information, knowledge and experience in industry during the process of product development. In this thesis, we propose a novel approach to the support of design, manufacturing, and inspection planning at the early stages of product development. The system we have developed is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) techniques, and will henceforth be referred to as the Smart Virtual Product Development (SVPD) system. This system comprises three primary modules, each of which has been developed to cater to a need for digital knowledge capture for smart manufacturing in the areas of product design, production planning, and inspection planning. The individual modules related to each of these areas in turn will henceforth be referred to as the design knowledge management (DKM) module, the manufacturing capability analysis and process planning (MCAPP) module, and the product inspection planning (PIP) module respectively. Together these modules are fully capable of supporting the five phases of advanced product quality planning (APQP). The SVPD system is a system that can store experiential knowledge relating to previous projects, and makes that knowledge available to a user who presents a relevant query in the future. Formal decisional events or experiences can be comprehensively represented in SOEKS using a unique combination of Variables, Functions, Constraints and Rules. A query based on objectives relevant to one of the modules mentioned above and comprised of variables and functions particular to those objectives is fed into the system, which then provides a list of potential solutions based on the experiential knowledge stored in the system. The user selects the most appropriate solution from among those provided, and that is stored in the system as an answer to similar queries. In the event that the system cannot provide a solution, an expert will then be consulted, and that expert’s decision will be manually inputted into the system and stored. The system, therefore, either updates itself or is updated manually each time a new decision is made. Our experimental results show that the SVPD system is an expert decisional support system and can play a vital role in the establishment of Industry 4.0. The system will benefit manufacturing organizations through the facilitation of product design, manufacturing, and inspection planning.

碩士
逢甲大學
企業管理學系
106
Since the concept of Industry 4.0 was proposed by Germany in 2013, smart manufacturing and smart factories have been highly valued by the industry. With the rapid development of Information communication technology, manufacturers are also actively launching digital transformation and investing in smart manufacturing. The application area covers the manufacturing and related information systems required for business operations to respond to the diverse needs of customers in a timely manner. The enterprise information system plays an important role in the process of manufacturing smart manufacturing and digital transformation. However, it has reviewed the related literatures of enterprise information systems over the years, and relatively less on issues related to smart manufacturing and enterprise information system integration. Therefore, this study aims to explore the role and adoption of enterprise information systems in the implementation of smart manufacturing-related enterprises in the process of enterprise transformation, as well as the impact of internal technology, task adaptation and external environment on the adoption of enterprise information systems. The design of this study is divided into two phases. The first phase is to discuss the relevant research areas with online critique tools to clarify the current research status and gaps of the enterprise information system. The second phase is empirical research, focusing on enterprises with smart manufacturing in Taiwan. The subjects were surveyed and distributed during the period from June to July 2018. A total of 62 questionnaire samples were collected. Data analysis was carried out with SPSS and SmartPLS2.0. The results show that the technical characteristics and tasks significantly affect the task-technical adaptability; while the organizational characteristics have no significant impact on the task-technical adaptation. Task-technical adaptation for enterprise information System adoption has no significant impact. Organizational characteristics do not affect the adoption of enterprise information systems. Environmental characteristics do not affect the adoption of enterprise information systems. From the research results, it can be inferred that the adaptation of scientific and functional characteristics is important in the process of promoting smart manufacturing. In addition to the literature on enterprise information systems, this research results in the scope of enterprise information systems and provides a link between smart manufacturing and enterprise information systems. Relevant research results will help relevant organizations to better understand the development trend of enterprise information systems and corporate transformation. The factors affecting the adoption of the enterprise information system in the process, in addition to the gaps in the research fields related to the information system, the practical aspects can also provide reference for the introduction or integration of relevant information systems.

One way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.

Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.

Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.

However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.

A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.

To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.

碩士
國立臺北科技大學
工業工程與管理系EMBA班
105
The manufacturing industry in China met unprecedented challenge and operation dilemma in past years. These are including labor shortage, continuous increasing wage, laws/regulation and integration with the world. The designated company has been performing excellent in the field by internal productivity/quality improvement, study Toyota production mode (Cell Production Line management) and implement the most updated Six Sigma management skill. However, the progress is far behind the sudden change of market. The management team is eagerly to find ways to expedite the transformation. Hereunder is the 5 years reforming program to DSM (D-CO Smart Manufacturing). The subject was made by case study method, reviewing the whole transition processes of designated production line from traditional into smart one. That includes programming onto new production line set up and produce/run the whole processes while promoting the DSM revolution, developing a specific/practicable assessment proposal especially at smart function achievement. By using case research combine with depth interview of related leaders、field observation and add in benefit verified pattern to ensure benefit index can be optimized, operation structure can be transited/upgraded. At the same time summarize the result and establish smart manufacturing system construction pattern. To provide related experience and pattern blueprint for continuous reform among industry.

碩士
中華科技大學
機電光工程研究所碩士班
101
The study mainly aimed to explore the intelligent control technology of dry laser etching for touch panel. Full-dry laser etching process of control panel integrated by intelligence allows assembly of various units (ends) in process into production line by any quantity and adapts to various production needs, enhancing operating ratio of production lines. Based on this structure, data of processed area have to be acquired online due to the errors occurring in the dry laser etching process. With a need to correct CAD/CAM immediately, cloud computing has been adopted to enhance the processing speed and performance and to raise the performance and yield rate of production line. Controller and server of all machines have adopted the same software system so as to facilitate homogeneous distributed parallel processing. By introducing and expanding the realization of self-organized ability of intelligent control system, the job scheduling problem was solved. Parallel computing structure was adopted to solve problems of slight adjustment of laser path control panel and high-speed computing for CAD/CAM path correction required in figure shaping. The focus of the study was on correction on the platform stability control so as to ensure precision of full travel. The sizes and locating places of processed objects were segmented into interval as frequency current by key points of various areas and Bode diagram observation and correction upon the echo was made. After the correction of controller PID, the correction of entire travel areas was performed and real measurement on comprehensive graph path was conducted. It was proved that the precision requirements could be achieved after the inspection of interferometer and a slight adjustment of computing within controller.

碩士
朝陽科技大學
資訊工程系
106
With the popularity of robotic manipulator and RFID technology as well as the ever-increasing cost of manpower, robotic manipulator has many conveniences. For example, some relatively simple or sophisticated assembly work or dangerous work can be handed over by robot manipulator to perform. Robot manipulator can improve product technology and quality, and most of these initial work can be done by a robotic manipulator. The precision and zero error of the manipulator arm has its own advantages for the control of the quality of the product and reduces the time and manpower consumed in the quality control. Industrial applications, the assembly, processing, welding, cutting, pressurization, cargo handling, electronics industry, testing, etc.. The use of robotic manipulator concentrates on automotive components, chemicals, rubber and plastics. By using robotic arm combined with RFID to save manpower and achieve logistics control, and the use of six-axis robotic arm processing can reduce human consumption, increase product quality and output.

碩士
國防大學理工學院
機械工程碩士班
107
Industry 4.0 is the development trend of the international manufacturing industry. It emphasizes the integration of existing industry-related technologies, sales and product experiences, and the establishment of a smart factory with adaptability, resource efficiency and human factors engineering. It will bring a major challenge to Taiwans industrial progress. The purpose of this research is to use the existing robotic arm, fixed milling machine, three-dimensional measuring instrument and other equipment in the laboratory to use the graphical data flow concept software LabVIEW programming program to integrate the system and set up a small intelligent manufacturing automation production line. Sensors such as noise meters and accelerometers are added to the processing platform to capture noise and vibration data during milling, and these measurement data are also captured and stored in real time using the LabVIEW program. Using these stored measurement to set up a neural network model, predict the operation of the tool in the process, and finally verify the rate of the effect to do the goal, and hope to complete a small intelligent manufacturing line through the system, to do a small variety of flexible production.

Dissertação de Mestrado em Engenharia Industrial
A quarta revolução industrial, ou Indústria 4.0, potencia diversas mudanças no sistema produtivo atual. Através da aplicação dos seus conceitos e ferramentas, facilita às organizações a recolha e análise, em tempo real, de dados da produção e uma eficiente sincronização e troca de informação entre fornecedores, produtores e clientes. Além disso, capacita as organizações para lidarem com o aumento da procura de produtos personalizados. Com a utilização de Smart Manufacturing Systems – sistemas de planeamento, controlo e programação da produção integrados, flexíveis e capazes de responder, em tempo real, às mudanças do mercado, do processo produtivo e da cadeia de abastecimento – as organizações serão capazes de realizar uma gestão da produção mais eficiente. Neste projeto de dissertação é apresentado o Sistema GenSYS como um Smart Manufacturing System e alguns dos seus principais conceitos são aplicados à indústria automóvel, mais concretamente à empresa ZF Friedrichshafen AG, de modo a demonstrar as vantagens da sua utilização em contexto real. Com a divisão da população de artigos em famílias de produtos e a criação de referências genéricas, o Sistema GenSYS permite uma redução do número de códigos de identificação necessários para representar a diversidade de produtos existentes na empresa em análise, comparativamente com os que seriam necessários nos modelos tradicionais de referenciação direta. Além disso, a aplicação do conceito de produção puxada e kanbans eletrónicos melhora a eficiência da programação da produção da empresa, uma vez que o Sistema GenSYS garante um fluxo eficaz no chão de fábrica, só alocando trabalhos aos postos quando todas as condições necessárias se encontrarem reunidas. Garante, ainda, que os componentes certos chegam ao posto correto e no momento em que são necessários, o que se reflete numa vantagem em termos de logística interna para a empresa. Com as projeções, a curto prazo, do estado do sistema produtivo, permite que a empresa possa agir, de forma proativa, sobre possíveis constrangimentos ou desvios em relação ao planeado. Na área da logística externa, permite um maior controlo e rastreabilidade dos produtos, em movimentos entre fornecedores e a empresa ou entre esta e o cliente.
The fourth industrial revolution, or Industry 4.0, promotes several changes in the current production system. Through the application of its concepts and tools, it facilitates organizations to collect and analyze, in real time, production data and an efficient synchronization and exchange of information between suppliers, producers and customers. In addition, it empowers organizations to deal with the increase in demand for personalized products. The usage of Smart Manufacturing Systems – integrated production planning, control and scheduling systems, flexible and capable of responding in real time to changes in the market, the production process and the supply chain – organizations will be able to carry out a more efficient production management. In this dissertation project, the GenSYS System is presented as a Smart Manufacturing System and some of its main concepts are applied to the automotive industry, more specifically to the company ZF Friedrichshafen AG, in order to demonstrate the advantages of its usage in a real context. By dividing the population of products into product families and using generic structures, the GenSYS System allows a reduction in the number of identification codes needed to represent the diversity of products existing in the company under analysis, compared to required in the traditional models of direct referencing. Moreover, the application of pulled production concept and electronic kanbans improves the efficiency of the company's production schedule, since the GenSYS System guarantees an efficient flow on the shop floor, only allocating jobs to stations when all the necessary conditions are met. Also ensures the right components reach the right station and when they are needed, which is reflected as an advantage in terms of internal logistics for the company. With the short-term projections of the state of the production system, it allows the company to act proactively on possible constraints or deviations from what was planned. In the area of external logistics, it allows greater control and traceability of products, in movements between suppliers and the company or between the company and the customer.

Smart manufacturing promotes the demand of a new interface for communication with virtual entities such as big data analysis model, digital twin, and autonomous software programs. Although ideal smart manufacturing pursues full automation by self-adoption and self-decision of autonomy, converging human intervention and collaboration with the autonomy have shown significant improvements on productivity and quality, and it expects more advancements on current manufacturing trend. This study aims how to combine human and autonomy based on current technical advancement of smart manufacturing. In detail, creating networks between the entities, developing a new interface for human-autonomy collaboration, and demonstrating the effectiveness of the collaboration are main research topics.

碩士
國立高雄應用科技大學
工業工程與管理系碩士在職專班
106
With the rapid development of Industry 4.0, smart factories have become a trend in modern industry. Through the integration of Virtualization, Internet of Things, smart devices and robots, the machine has been able to provide intelligent services and production can be replaced by machines. However, how the equipment of old factory plant is integrated directly through the Internet of Things, the construction of system planning, and management, in order to reduce cost of manpower, improve quality, and establish a good company image,will be the focus of this study. This case study uses “A” company as an example. The baking oven in the factory is old and the operation mode requires manual operation and judgment, resulting in waste of manpower and failure by human judgment. Therefore, the core of industry 4.0 application was introduced to build a smart manufacturing factory. The IoT elements built by the smart factory are upgraded. The original old oven was upgraded to automate chinery, and the information parameters were maintained for different products. The machine and parameters were linked through the network connection. After the machine platform confirms the product information, the data is passed to the system for judgment. As the system compares the oven king parameters correctly, the system passes the parameters back to the machine. The machine station starts the operation and processing the product. Case A company establishes an architecture model of intelligent manufacturing, integrates the work processes of factories and related departments, and replaces manpower by machines to reduce manpower. By reducing the manpower from three operators per shift to one operator per shift, the company can save NT$320,000 per month. Moreover, replacing human judgement by system judgement reduced the average number of abnormal cases by 10 per month to zero. Handling by machines also reduced safety incidents to 0. The smart factory operation improved the company's good image and resulted zero finding from customer's audits. Keywords : Smart Factory, Industry 4.0, Semiconductor

碩士
國立臺北科技大學
工業工程與管理系
106
In a competitive global market, manufacturing managers face more difficult challenges than before. The market demands are a customized products and manufacturing managers need to provide high quality, low price, short delivery products and reduce inventory to maintain competitiveness. Manufacturing managers must reduce waste, increase production efficiency, and obtain higher benefits, but information on the manufacturing cannot be instantly returned so that it is impossible to master the manufacturing loss and product quality and cause the failure to deliver on time. Therefore, we must master the progress of production to achieve dynamic adjustment of production schedules and respond customer needs quickly and improve the flexibility and adaptability. The purpose of this study is to propose an algorithm for monitoring the continuous production states of machines, machine signal segmentation analysis(MSSA), combining Hilbert-Huang Transform (HHT) and K-means clustering to define signal segmentation points, and combining hidden markov models (HMMs) to determine manufacturing states. This algorithm is used to monitor the machine states and control the production progress. and using machine states calculate availability and production quantity. In addition, monitoring machine status can reduce manufacturing losses and waste and reduce machine idle time to manage production schedule immediately and enhance the flexibility and flexibility of the manufacturing.

碩士
國立高雄科技大學
資訊工程系
107
With the rapid development of science and technology and the progress of the times, the technology of the Internet of Things has become more and more mature. In the past few years, a new wave of scientific and technological revolutions and industrial changes has emerged in the world. Developed countries have followed the trend and have thrown out the stimulus for real economic growth. The national strategy and plan hopes to regain the competitive advantage in manufacturing through technological advancement and industrial policy adjustment. Among them, Germany, one of the major industrial countries, proposed the “Industry 4.0” reform method, which was designed in accordance with the industrial characteristics of its own country. The main core is intelligent manufacturing, through embedded processors, memories, sensors and communications. Modules, which connect equipment, products, raw materials, and software, so that products and different production equipment can be interconnected and exchange information. In other words, Germany's Industry 4.0 can correct errors, optimize and control and adjust production lines in the future. Because of its industrial type, Taiwan's small and medium-sized enterprises are not able to have sufficient funds, product information and customer information, just like Germany or international companies, to make the factory complete and systematically intelligent to enhance competition. We need to design different smart factory solutions for different types and conditions of different factories. This paper will use a certain enzyme factory in Taiwan as a case to design a prototype of a smart factory plan to solve the current problems of the plant. To increase productivity has brought more benefits.

碩士
國立臺北科技大學
管理學院EMBA泰國專班
107
In recent years, under the Internet era where national policies have actively promoted various technologies, it is imperative for smart manufacturing to lead the transformation of manufacturing industry, with the aim of reducing production and maintenance costs, improving production efficiency, responding to flexible production and solving problems such as lack of work. The process of intelligentization will reshape the management mechanism of the manufacturing industry, and will change the appearance of the industrial chain from then on. The main research of this paper is to find innovative business models through business model research to establish a standardized intelligent manufacturing process. Based on the past literature and the business model of Jiugongge, this study analyzes the various factors of the smart manufacturing system solution business model and the in-depth interviews with relevant industry experts to clarify the various factors of the business model. Finally, this study will use the DPE automation solution company case to illustrate the results of the nine-square grid analysis of this business model. According to the research results, it is obvious that Thailand is keen to become a big country in science and technology. Therefore, the promotion of smart manufacturing systems is quite enjoyable. Enterprises can reduce labor costs and improve yields, and turn their focus to investment technology research and development. On the top, make better resource allocation to achieve a win-win situation.

碩士
國立臺北科技大學
機械工程系機電整合碩士班
107
This research aims to build a small scale autonomous factory based on the Cyber Physical System (CPS) and Internet of Things (IoT).This factory has 3 CNC laser engraving machines as the core platform and smart manufacturing technologies. The developed system uses communication network to integrate the physical manufacturing machines and information of customized orders by various software processing, data management, and automation techniques to realize a cyber-physic system as well as a flexible manufacturing system. The system is designed academic research on the Industry 4.0 and related technologies. Besides the abovementioned automation, production data collection via web-link devices are also implemented to provide real-time facility monitoring and big data analysis for quality assurance, production management and other purposes. This thesis consists of two parts, system implementation and data analysis. The system implementation is based on the smart manufacturing and focused on the flexibility production, cyber-physic system and IoT. The implemented system has 3 CNC laser engraving machines with different laser power generators to mimic the variance of machines in the real world. Furthermore, flexible and customized designed tags are chosen to be the products so that the system has to face the challenges of flexibility. An internet-based order-making interface program will also be integrated. For the data analysis, a user interface is created to collect data from CNC machines and save it in the cloud server for analysis. Based on the collected data, machine efficiency and health can be predicted and feed-backed immediately to adjust the production settings for quality improvement and the prepare for preventive maintenance and monitoring of machines.

碩士
國立臺北科技大學
管理學院工業工程與管理EMBA專班
107
Taiwan’s manufacturing industry is subject to international competition and changes in the economic environment. It faces not only the aging of the working population, the lack of industrial work, but also the shorter product life cycle, more diverse customer demand, and rising The impact of labor costs, etc., in order to solve the problem of manpower demand, improve the per capita output value of productivity and accelerate industrial transformation and upgrading, and enhance the competitiveness of enterprises, is the key to sustainable business. Industry 4.0 provides the best tools and turnarounds. The concept is to solve the problem of manufacturing response speed, productivity improvement and high flexibility through intelligent automation applications, thereby increasing productivity and enhancing corporate competitiveness. This study focuses on the research and discussion of the introducing smart manufacturing into small and medium-sized manufacturing industries, and explores the impact of the introduction of Industry 4.0 on the manufacturing industry of SMEs. By collating relevant literature and case studies, integrating the opinions of different groups, and then introducing the industry into case companies. 4.0 as the subject of discussion, and analysis of the different traditional manufacturing management issues brought by it as a reference for the SME manufacturing industry.

碩士
國立高雄科技大學
資訊工程系
108
Smart manufacturing is a popular issue in recent years, it had been three revolution of industrial during the past. Start from the first revolution of industrial, called industrial 1.0 ,it's a machined age cause by the introduction of steam engine, then the second revolution of industrial, called industrial 2.0, use electric power to make a great amount of product, and the third revolution of industrial, called industrial 3.0, use PLC/CNC controller and robotic arm to improve the Automated control system, and now is the fourth revolution of industrial, Combine network and hardware called industrial 4.0, it's the background concept of the Smart manufacturing. There is lot of activity to research and improve the development of smart manufacturing, but stand in those industry's shoes, there is no established concept on smart manufacturing, it's said to solved the different demand of industry. But there is a common point that's to break the old view of industry, and developing a humanity way of manufacturing. The pourpose of this research is to design and implement a cloud-based prototype system for smart manufacturing execution. combind the concept of smart manufacturing, useing HMVC construst to build a management system of business. Useing cloud technology to transferthe firsthand information and analysis the data, to find the potential sales approach of salesperson,it's can also improve the industrial value of the related products.

碩士
國立高雄科技大學
資訊工程系
107
Nowadays, we live in a period that people highly seek digitalization and artificial intelligence. The Industrial structure and consumption patterns in the world is hugely changing because of various new technologies developing. The industries, products, and services applied new skills widely appear in a short time. The new skills are continuously innovating, developing, and expanding their application area, making the existing industry business models constantly transforming. Every industry expects that artificial intelligence would optimize the supply chain and big data analysis would make enterprises able to forecasting and quickly grasping the clients’ demands. Then the enterprise could affiliate smart manufacturing to offer faster service and better products when trading in a more efficient way. Therefore, they can provide great trading experience for clients, and obtain great profit. The purpose of this thesis is to study how to Cloudization and Systematization the data of a traditional transaction, applying machine learning and big data technology, combined with the website front-end technology and the database management system to design and to implement a smart business system, and under such a framework how to implement business expects like reducing time cost, increasing work efficiency and turnover rate, and analyzing visiting modes smartly, and then affiliate smart manufacturing to apply artificial intelligence to all the parts of supply chain. When trading with various group of clients, the process of getting the client’s demand until producing products must be smarter in order to make the enterprise quickly set the business strategy and goal for every client group. This Research explores how to introduce machine learning and big data technologies into a business system, and further integrates the data from clients and analyze the data to promote the decision-making wisdom of the enterprise.

碩士
國立臺灣科技大學
自動化及控制研究所
107
Nowadays, the printing system using photo curing technology has a fixed thickness per layer, and the speed is the same during the pulled up stage. It will result in uncontrollable pulling force and can’t print the object efficiently. First, this study investigates the influence of lifting speed and layer area on pulling force during the lifting process for a high-resolution LCD desktop additive manufacturing system with bottom-illumination source. In this study, a load cell is installed on the top of forming shaft for monitoring the pulling force of a built object during the lifting stage. With the load cell, the pulling force was measured with different graphic areas and different lifting speeds. The appropriate lifting speed of z-axis for each layer is calculated by a math model, which leads to efficiently build the molded object. Then a new printing G code will be generated, and the mask image will be transferred to the high-resolution LCD screen. Finally, the photo curing resin and the load cell will be used to achieve the printed workpiece in a short time. Finally, this study used three different heights of graphics for actual printing and found that it can reduce printing time about 13%. Then this study also develops a pulling force monitoring program which can judge whether the printing has an abnormality by using the pulling force during the lifting process. If the printing fails, the monitoring system will send an alarm message to the user. The user can deal with the situation immediately and can also view the printed results via the camera on web.