Дисертації з теми "Smart Manufacturing Systems"
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Nilsson, Felix. "Image analysis for smart manufacturing." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39856.
Повний текст джерелаIndustriell 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.
Diaz, Castañeda Jenny Lorena. "Advanced energy management/control strategies for smart manufacturing systems." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2020. http://hdl.handle.net/10803/672058.
Повний текст джерелаEsta 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.
Jeong, Hyunsoo. "Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106252.
Повний текст джерелаCataloged 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
Rudberg, Zacharias, and Oscar Sandelin. "Impact on manufacturing execution systems through the use of smart connected devices." Thesis, KTH, Industriell produktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246137.
Повний текст джерелаDen 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.
Dreyer, Sonja [Verfasser]. "Digital transformation in the manufacturing industry : business models and smart service systems / Sonja Dreyer." Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2020. http://d-nb.info/1205878491/34.
Повний текст джерелаWilliams, David Lee. "The Conversion of Manual Machining Equipment into Smart, Connected Systems with Real-Time Monitoring and Issue Identification Capabilities." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8542.
Повний текст джерелаCoyne, Bradley. "The 21 st Century Manufacturer: : The Role of Smart Products in the Transition from a Product to a Service Based Focus in Manufacturing Industries." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-15911.
Повний текст джерелаTomas, Adam. "Product Digitalization from the Perspective of an Established Manufacturing Firm." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104945.
Повний текст джерелаLiebert, Andreas. "Industry 4.0 – the intended impact of Cyber Physical Systems in a Smart Factory on the daily business processes : A Study on BMW (UK) Manufacturing Limited." Thesis, Linnéuniversitetet, Institutionen för organisation och entreprenörskap (OE), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-54407.
Повний текст джерелаMirza, Helen, and Rade Nikolic. "Hur förändrar smart teknik resurseffektiviteten i fordonsbranschen? : En studie av hur Cyber-Physical Systems och Internet of Things påverkar resurseffektiviteten i personbilsbranschen." Thesis, KTH, Industriell produktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255153.
Повний текст джерелаToday, 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.
Giustozzi, Franco. "STEaMINg : semantic time evolving models for industry 4.0 Stream reasoning to improve decision-making in cognitive systems Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR13.
Повний текст джерелаIn 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
KLINGA, PETTER, and ERIK STORÅ. "Vilka utmaningar och hinder möter större tillverkande företag vid implementering av digital och smart teknik samt hur kan dessa åtgärdas? : En studie kring den pågående digitala transformationen av tillverkningsindustrin." Thesis, KTH, Industriell produktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233206.
Повний текст джерелаThe 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.
Nessle, Åsbrink Marcus. "A case study of how Industry 4.0 will impact on a manual assembly process in an existing production system : Interpretation, enablers and benefits." Thesis, KTH, Industriell produktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288514.
Повний текст джерелаBegreppet 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.
Candell, Richard. "Performance Estimation, Testing, and Control of Cyber-Physical Systems Employing Non-ideal Communications Networks." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCK017.
Повний текст джерелаWireless 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
Cao, Qiushi. "Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.
Повний текст джерелаIn 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
LE, DAVID. "SMART MANUFACTURING DIAGNOSTIC SYSTEM (SMDS) CREATING AN ASSESSMENT PROCESS FOR SMALL TO MEDIUM SIZE MANUFACTURERS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085600917.
Повний текст джерелаCuozzo, Giampaolo. "A Wireless Protocol for Smart Manufacturing using LoRa at 2.4 GHz." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
Знайти повний текст джерелаLe, David. "Smart manufacturing diagnostic system (SMDS) creating ana assessment process for small to medium size manufacturers." Cincinnati, Ohio : University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1085600917.
Повний текст джерелаAlrayes, Ali Said. "Transmission system overvoltage mitigation through the use of distributed generation (DG) smart inverters." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126993.
Повний текст джерелаThesis: 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
Lu, Zheyi. "The Architecture of Blockchain System across the Manufacturing Supply Chain." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239029.
Повний текст джерелаI 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.
Nemrow, Andrew Craig. "Implementing an IIoT Core System for Simulated Intelligent Manufacturing in an Educational Environment." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8822.
Повний текст джерелаJoseph, Anand Emmanuel, and Zafra Luis Carlos Chica. "Evaluation of a medium-sized enterprise’s performance by data analysis : Introducing innovative smart manufacturing perspectives." Thesis, KTH, Industriell produktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261351.
Повний текст джерелаSmå 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.
Ramadan, Muawia [Verfasser], and Bernd [Akademischer Betreuer] Noche. "RFID-Enabled Dynamic Value Stream Mapping for Smart Real-Time Lean-Based Manufacturing System / Muawia Ramadan. Betreuer: Bernd Noche." Duisburg, 2016. http://d-nb.info/1090785445/34.
Повний текст джерелаColamonaco, Matteo. "Industry 4.0: stato dell'arte dell'implementazione a livello internazionale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14016/.
Повний текст джерелаErkki, Robert, and Philip Johnsson. "Quality Data Management in the Next Industrial Revolution : A Study of Prerequisites for Industry 4.0 at GKN Aerospace Sweden." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69341.
Повний текст джерелаJakimov, Jan. "Návrh na realizaci projektu ve společnosti TOROLA design s.r.o." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222835.
Повний текст джерела(6849257), Harsha Naga Teja Nimmala. "Smart Manufacturing Using Control and Optimization." Thesis, 2019.
Знайти повний текст джерелаEnergy 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.
Yu-ChenLai and 賴宥呈. "Data Analytics Framework for Smart Manufacturing Execution Systems." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fqtsw7.
Повний текст джерела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.
"Real-time Analysis and Control for Smart Manufacturing Systems." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.62823.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Industrial Engineering 2020
Trindade, Alberto Luís Bastos. "Tooling 4G - Advanced Tools for Smart Manufacturing." Master's thesis, 2021. http://hdl.handle.net/10400.8/5552.
Повний текст джерелаShu, Hsin-Yu, and 許信裕. "The Application of Collaborative Design and Smart Systems in Manufacturing- A Case Study of Solen Electric Company." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8ebrtn.
Повний текст джерела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.
Ahmed, Muhammad Bilal. "Smart virtual product development system." Thesis, 2021. http://hdl.handle.net/1959.13/1420676.
Повний текст джерелаThe 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.
LI, HONG-HUI, and 李虹慧. "A Study on the Impact Factors of Enterprise Information Systems and Smart Manufacturing by Using the Task-Technology Fit Theory." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m47e36.
Повний текст джерела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.
(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Знайти повний текст джерелаOne 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.
Huang, Ru-Kuen, and 黃陸坤. "The Construction of Smart Manufacturing System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/km36z5.
Повний текст джерела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.
Lin, Bo Shu, and 林柏樹. "Touch panel Smart Manufacturing System Control Technology." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/46469381232050908677.
Повний текст джерела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.
LIAO, YI-LING, and 廖翊伶. "Smart Manufacturing System Combining Six-Axis Robot Manipulator and RFID." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9gy445.
Повний текст джерела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.
LIN, HSIN-CHIH, and 林信志. "On the Development of Smart Manufacturing System and Integration Planning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/sab826.
Повний текст джерела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.
Faria, Carolina Maria Fernandes de. "Proposta para aplicação do Smart Manufacturing System GenSYS na indústria automóvel." Master's thesis, 2020. http://hdl.handle.net/1822/64537.
Повний текст джерелаA 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.
(11153499), Huitaek Yun. "Immersive and interactive cyber-physical system." Thesis, 2021.
Знайти повний текст джерелаLIU, HSIEN-CHIH, and 劉賢治. "The Smart Factory Oven system of Semiconductor Manufacturing-Example of A Company." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/hmy6ru.
Повний текст джерела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
Hung, Chia-Yu, and 洪佳妤. "Integrated Machine Signal Segmentation Analysis for Process States of Smart Manufacturing System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qe9v8p.
Повний текст джерела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.
Wu, Zheng-Nan, and 吳政南. "Prototype System for Smart Manufacturing Factory Based on Cloud and IoT Technologies." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jm9xub.
Повний текст джерела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.
HUANG, JUI-CHING, and 黃瑞慶. "Discussion on the Business Model of Smart Manufacturing System Solution Type Enterprise." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/up5es4.
Повний текст джерела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.
CHEN, WEI-CHUNG, and 陳為仲. "Implementation of an Intelligent Production System based on the Smart Manufacturing Technologies." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/89sj6s.
Повний текст джерела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.
CHENG, I.-CHEN, and 鄭伊甄. "Research on Integrating Smart Manufacturing System in Small and Medium-sized Enterprises Factories." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/46u9v8.
Повний текст джерела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.
ZHENG, LI-SHENG, and 鄭禮聖. "Design and Implementation of a Cloud-based Prototype System for Smart Manufacturing Execution." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hmgcpj.
Повний текст джерела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.
KUO, CHIEN-TING, and 郭建廷. "Implementation of Smart Manufacturing Information Management System based on Cloud Edge Computing Technology." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/628jav.
Повний текст джерела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.
Khakifirooz, Marzieh, and 馬之雅. "A Framework for Intelligent Decision Support System for Smart Manufacturing to Empower Industry 4.0: The Illustration of Semiconductor Manufacturing." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/cwd3yz.
Повний текст джерелаChuang, Shu-Hao, and 莊書豪. "Study of Smart Pulling Force Monitor for High Resolution LCD Desktop Additive Manufacturing System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4gwwwm.
Повний текст джерела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.