Dissertations / Theses on the topic 'Knowledge based smart manufacturing system'
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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.
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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 synthétiquesIn 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
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蕭世良 and Sai-leung Siu. "A knowledge based process planning system for prismatic parts." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31232784.
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Govindan, Saravana. "A task based manufacturing knowledge maintenance method." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/12414.
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The effective use of computer based tools to support decision making in manufacturing industry is critical to business success. One of the most critical areas is during product design and especially in design for manufacture. This research will help in understanding of how manufacturing knowledge can be effectively maintained for an existing knowledge base. The work will use modern product lifecycle management tools in combination with a knowledge based environment in order to explore the effectiveness of the methods produced. This work is a part of the SAMULET (Strategic Affordable Manufacturing in the UK through Leading Environmental Technologies) research program and was done in association with an aerospace manufacturing company. The main focus of this research is to define a novel method for maintaining the machining knowledge associated with manufacturing of Xtra Wide Body (XWB) High Pressure (HP) turbine blade. The four main elements explained in this thesis are, a) the literature review done on knowledge management and knowledge maintenance, b) industrial investigation done on a manufacturing facility, c) detailed explanation of a novel manufacturing knowledge maintenance method d) four iterative case studies used for the evaluation and iterative improvement of the method. The research concludes that the aspect of knowledge maintenance is important. It is imperative to set out a formalised and mandated knowledge maintenance process in an organisation to keep the knowledge up-to-date and relevant. It has been shown that a novel task based knowledge maintenance method comprising a Knowledge Maintenance Process (KMP) and a Knowledge Maintenance Template (KMT) provides an effective route to knowledge maintenance. Three maintenance tasks, check relevancy, knowledge filtering, and integrity checking have been considered in detail for successful knowledge maintenance. Four iterative case studies have been conducted for the experimental evaluation of the maintenance method. As the result of these evaluations a novel method for maintaining the machining knowledge of XWB HP turbine blade was defined.
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Roy, Asok K. "Development of a knowledge based vision system for automated inspection." Thesis, Glasgow Caledonian University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363131.
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Wondoloski, Karen M. "A knowledge-based cell controller and its integration in a manufacturing system." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/23341.
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Sun, Qi-zhi. "Knowledge-based interactive real-time control system in product-focused manufacturing environment." Thesis, University of Portsmouth, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292501.
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Al-Awadhi, Waleed. "Integrating machine grouping and layout by using knowledge based system approach." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242982.
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Al-Khawaldeh, Mustafa Awwad Salem. "Ubiquitous robotics system for knowledge-based auto-configuration system for service delivery within smart home environments." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10202.
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The future smart home will be enhanced and driven by the recent advance of the Internet of Things (IoT), which advocates the integration of computational devices within an Internet architecture on a global scale [1, 2]. In the IoT paradigm, the smart home will be developed by interconnecting a plethora of smart objects both inside and outside the home environment [3-5]. The recent take-up of these connected devices within home environments is slowly and surely transforming traditional home living environments. Such connected and integrated home environments lead to the concept of the smart home, which has attracted significant research efforts to enhance the functionality of home environments with a wide range of novel services. The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. The trend whereby the development of smart home services is decoupled from that of smart home devices increases the complexity of this task. As such, it is desirable that smart home services are developed and deployed independently, rather than pre-bundled with specific devices, although it must be recognised that this is not always practical. Moreover, systems need to facilitate the deployment process and cope with any changes in the target environment after deployment. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. Although significant research has been directed towards achieving auto-configuration, none of the existing solutions is sufficient to achieve auto-configuration within smart home environments. All such solutions are considered incomplete, as they lack the ability to meet all smart home requirements efficiently. These requirements include the ability to adapt flexibly to new and dynamic home environments without direct user intervention. Fulfilling these requirements would enhance the performance of smart home systems and help to address cost-effectiveness, considering the financial implications of the manual configuration of smart home environments. Current configuration approaches fail to meet one or more of the requirements of smart homes. If one of these approaches meets the flexibility criterion, the configuration is either not executed online without affecting the system or requires direct user intervention. In other words, there is no adequate solution to allow smart home systems to adapt dynamically to changing circumstances, hence to enable the correct interconnections among its components without direct user intervention and the interruption of the whole system. Therefore, it is necessary to develop an efficient, adaptive, agile and flexible system that adapts dynamically to each new requirement of the smart home environment. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Software Robot (Sobot) for Smart Home Environments, which utilises the Sobot to achieve auto-configuration of the system. The research work was conducted under the Distributed Integrated Care Services and Systems (iCARE) project, which was designed to accomplish and deliver integrated distributed ecosystems with a homecare focus. The auto-configuration Sobot which is the focus of this thesis is a key component of the iCARE project. It will become one of the key enabling technologies for generic smart home environments. It has a profound impact on designing and implementing a high quality system. Its main role is to generate a feasible configuration that meets the given requirements using the knowledgebase of the smart home environment as a core component. The knowledgebase plays a pivotal role in helping the Sobot to automatically select the most appropriate resources in a given context-aware system via semantic searching and matching. Ontology as a technique of knowledgebase representation generally helps to design and develop a specific domain. It is also a key technology for the Semantic Web, which enables a common understanding amongst software agents and people, clarifies the domain assumptions and facilitates the reuse and analysis of its knowledge. The main advantages of the Sobot over traditional applications is its awareness of the changing digital and physical environments and its ability to interpret these changes, extract the relevant contextual data and merge any new information or knowledge. The Sobot is capable of creating new or alternative feasible configurations to meet the system's goal by utilising inferred facts based on the smart home ontological model, so that the system can adapt to the changed environment. Furthermore, the Sobot has the capability to execute the generated reconfiguration plan without interrupting the running of the system. A proof-of-concept testbed has been designed and implemented. The case studies carried out have shown the potential of the proposed approach to achieve flexible and reliable auto-configuration of the smart home system, with promising directions for future research.
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Mahmood, Tariq. "Knowledge-based process planning and design system for the cold forging of steel." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264352.
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Chun-Kit, Kwong. "A computer-aided concurrent design system." Thesis, University of Warwick, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263309.
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Rashidy, Haitham. "Knowledge-based quality control in manufacturing processes with application to the automotive industry /." München : Herbert Utz, 2009. http://opac.nebis.ch/cgi-bin/showAbstract.pl?u20=9783831608621.
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Rashidy, Haitham. "Knowledge-based quality control in manufacturing processes with application to the automotive industry." München Utz, 2008. http://d-nb.info/991264630/04.
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Wang, Wei. "A knowledge based modelling system for the design and evaluation of flexible manufacturing facilities." Thesis, Loughborough University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328603.
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Zhu, Chun Bao. "Optimisation of the grinding process using process modelling and knowledge based system approach." Thesis, University of the West of England, Bristol, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334546.
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Siddique, Mohammad. "A knowledge-based system for process planning in a seamless steel tube plant." Thesis, Aston University, 1990. http://publications.aston.ac.uk/11889/.
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The thesis describes the work carried out to develop a prototype knowledge-based system 'KBS-SETUPP' to generate process plans for the manufacture of seamless tubes. The work is specifically related to a plant in which hollows are made from solid billets using a rotary piercing process and then reduced to required size and finished properties using the fixed plug cold drawing process. The thesis first discusses various methods of tube production in order to give a general background of tube manufacture. Then a review of the automation of the process planning function is presented in terms of its basic sub-tasks and the techniques and suitability of a knowledge-based system is established. In the light of such a review and a case study, the process planning problem is formulated in the domain of seamless tube manufacture, its basic sub-tasks are identified and capabilities and constraints of the available equipment in the specific plant are established. The task of collecting and collating the process planning knowledge in seamless tube manufacture is discussed and is mostly fulfilled from domain experts, analysing of existing manufacturing records specific to plant, textbooks and applicable Standards. For the cold drawing mill, tube-drawing schedules have been rationalised to correspond with practice. The validation of such schedules has been achieved by computing the process parameters and then comparing these with the drawbench capacity to avoid over-loading. The existing models cannot be simulated in the computer program as such, therefore a mathematical model has been proposed which estimates the process parameters which are in close agreement with experimental values established by other researchers. To implement the concepts, a Knowledge-Based System 'KBS- SETUPP' has been developed on Personal Computer using Turbo- Prolog. The system is capable of generating process plans, production schedules and some additional capabilities to supplement process planning. The system generated process plans have been compared with the actual plans of the company and it has been shown that the results are satisfactory and encouraging and that the system has the capabilities which are useful.
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Law, Hang-Wai. "Knowledge-based computer-aided process planning system for the manufacture of bare printed circuit board." Thesis, Loughborough University, 1994. https://dspace.lboro.ac.uk/2134/27414.
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This thesis focuses on the use of a knowledge-based computer aided system for the task of bare printed circuit board (PCB) process planning. To achieve this task, a knowledge-based computer system has been developed in which process plans can be generated automatically. The planning decisions are based on board requirements, customer general specifications and product quality standards.
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Razfar, Mohammad Reza. "Development of a knowledge based system for the selection of cutting tools and conditions for milling." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319419.
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Valdes, Francisco Javier. "Manufacturing compliance analysis for architectural design: a knowledge-aided feature-based modeling framework." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54973.
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Given that achieving nominal (all dimensions are theoretically perfect) geometry is challenging during building construction, understanding and anticipating sources of geometric variation through tolerances modeling and allocation is critical. However, existing building modeling environments lack the ability to support coordinated, incremental and systematic specification of manufacturing and construction requirements. This issue becomes evident when adding multi-material systems produced off site by different vendors during building erection. Current practices to improve this situation include costly and time-consuming operations that challenge the relationship among the stakeholders of a project. As one means to overcome this issue, this research proposes the development of a knowledge-aided modeling framework that integrates a parametric CAD tool with a system modeling application to assess variability in building construction. The CAD tool provides robust geometric modeling capabilities, while System Modeling allows for the specification of feature-based manufacturing requirements aligned with construction standards and construction processes know-how. The system facilitates the identification of conflicting interactions between tolerances and manufacturing specifications of building material systems. The expected contributions of this project are the representation of manufacturing knowledge and tolerances interaction across off-site building subsystems to identify conflicting manufacturing requirements and minimize costly construction errors. The proposed approach will store and allocate manufacturing knowledge as Model-Based Systems Engineering (MBSE) design specifications for both single and multiple material systems. Also, as new techniques in building design and construction are beginning to overlap with engineering methods and standards (e.g. in-factory prefabrication), this project seeks to create collaborative scenarios between MBSE and Building Information Modeling (BIM) based on parametric, simultaneous, software integration to reduce human-to-data translation errors, improving model consistency among domains.
Important sub-stages of this project include the comprehensive review of modeling and allocation of tolerances and geometric deviations in design, construction and engineering; an approach for model integration among System Engineering models, mathematical engines and BIM (CAD) models; and finally, a demonstration computational implementation of a System-level tolerances modeling and allocation approach.
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Kotevska, Olivera. "Learning based event model for knowledge extraction and prediction system in the context of Smart City." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM005/document.
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Des milliards de «choses» connectées à l’internet constituent les réseaux symbiotiques de périphériques de communication (par exemple, les téléphones, les tablettes, les ordinateurs portables), les appareils intelligents, les objets (par exemple, la maison intelligente, le réfrigérateur, etc.) et des réseaux de personnes comme les réseaux sociaux. La notion de réseaux traditionnels se développe et, à l'avenir, elle ira au-delà, y compris plus d'entités et d'informations. Ces réseaux et ces dispositifs détectent, surveillent et génèrent constamment une grande uantité de données sur tous les aspects de la vie humaine. L'un des principaux défis dans ce domaine est que le réseau se compose de «choses» qui sont hétérogènes à bien des égards, les deux autres, c'est qu'ils changent au fil du temps, et il y a tellement d'entités dans le réseau qui sont essentielles pour identifier le lien entre eux.Dans cette recherche, nous abordons ces problèmes en combinant la théorie et les algorithmes du traitement des événements avec les domaines d'apprentissage par machine. Notre objectif est de proposer une solution possible pour mieux utiliser les informations générées par ces réseaux. Cela aidera à créer des systèmes qui détectent et répondent rapidement aux situations qui se produisent dans la vie urbaine afin qu'une décision intelligente puisse être prise pour les citoyens, les organisations, les entreprises et les administrations municipales. Les médias sociaux sont considérés comme une source d'information sur les situations et les faits liés aux utilisateurs et à leur environnement social. Au début, nous abordons le problème de l'identification de l'opinion publique pour une période donnée (année, mois) afin de mieux comprendre la dynamique de la ville. Pour résoudre ce problème, nous avons proposé un nouvel algorithme pour analyser des données textuelles complexes et bruyantes telles que Twitter-messages-tweets. Cet algorithme permet de catégoriser automatiquement et d'identifier la similarité entre les sujets d'événement en utilisant les techniques de regroupement. Le deuxième défi est de combiner les données du réseau avec diverses propriétés et caractéristiques en format commun qui faciliteront le partage des données entre les services. Pour le résoudre, nous avons créé un modèle d'événement commun qui réduit la complexité de la représentation tout en conservant la quantité maximale d'informations. Ce modèle comporte deux ajouts majeurs : la sémantiques et l’évolutivité. La partie sémantique signifie que notre modèle est souligné avec une ontologie de niveau supérieur qui ajoute des capacités d'interopérabilité. Bien que la partie d'évolutivité signifie que la structure du modèle proposé est flexible, ce qui ajoute des fonctionnalités d'extensibilité. Nous avons validé ce modèle en utilisant des modèles d'événements complexes et des techniques d'analyse prédictive. Pour faire face à l'environnement dynamique et aux changements inattendus, nous avons créé un modèle de réseau dynamique et résilient. Il choisit toujours le modèle optimal pour les analyses et s'adapte automatiquement aux modifications en sélectionnant le meilleur modèle. Nous avons utilisé une approche qualitative et quantitative pour une sélection évolutive de flux d'événements, qui réduit la solution pour l'analyse des liens, l’optimale et l’alternative du meilleur modèleBillions of “things” connected to the Internet constitute the symbiotic networks of communication devices (e.g., phones, tablets, and laptops), smart appliances (e.g., fridge, coffee maker and so forth) and networks of people (e.g., social networks). So, the concept of traditional networks (e.g., computer networks) is expanding and in future will go beyond it, including more entities and information. These networks and devices are constantly sensing, monitoring and generating a vast amount of data on all aspects of human life. One of the main challenges in this area is that the network consists of “things” which are heterogeneous in many ways, the other is that their state of the interconnected objects is changing over time, and there are so many entities in the network which is crucial to identify their interdependency in order to better monitor and predict the network behavior. In this research, we address these problems by combining the theory and algorithms of event processing with machine learning domains. Our goal is to propose a possible solution to better use the information generated by these networks. It will help to create systems that detect and respond promptly to situations occurring in urban life so that smart decision can be made for citizens, organizations, companies and city administrations. Social media is treated as a source of information about situations and facts related to the users and their social environment. At first, we tackle the problem of identifying the public opinion for a given period (year, month) to get a better understanding of city dynamics. To solve this problem, we proposed a new algorithm to analyze complex and noisy textual data such as Twitter messages-tweets. This algorithm permits an automatic categorization and similarity identification between event topics by using clustering techniques. The second challenge is combing network data with various properties and characteristics in common format that will facilitate data sharing among services. To solve it we created common event model that reduces the representation complexity while keeping the maximum amount of information. This model has two major additions: semantic and scalability. The semantic part means that our model is underlined with an upper-level ontology that adds interoperability capabilities. While the scalability part means that the structure of the proposed model is flexible in adding new entries and features. We validated this model by using complex event patterns and predictive analytics techniques. To deal with the dynamic environment and unexpected changes we created dynamic, resilient network model. It always chooses the optimal model for analytics and automatically adapts to the changes by selecting the next best model. We used qualitative and quantitative approach for scalable event stream selection, that narrows down the solution for link analysis, optimal and alternative best model. It also identifies efficient relationship analysis between data streams such as correlation, causality, similarity to identify relevant data sources that can act as an alternative data source or complement the analytics process
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O'Kane, James Francis. "The use of a dynamic database within a knowledge-based system for analysing reactive scheduling issues in a FMS." Thesis, Staffordshire University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318342.
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De, Souza Antonio Artur. "Developing a knowledge-based decision support system to aid make-to-order companies in cost estimation and pricing decisions." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296684.
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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.
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Moud, Nawawi Mohd Kamal. "The development of a hybrid knowledge-based Collaborative Lean Manufacturing Management (CLMM) system for an automotive manufacturing environment : the development of a hybrid Knowledge-Based (KB)/ Analytic Hierarchy Process (AHP)/ Gauging Absences of Pre-Requisites (GAP) Approach to the design of a Collaborative Lean Manufacturing Management (CLMM) system for an automotive manufacturing environment." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/3353.
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The automotive manufacturing facility is extremely complex and expensive system. Managing and understanding the dynamics of automotive manufacturing is a challenging endeavour. In the current era of dynamic global competition, a new concept such as Collaborative Lean Manufacturing Management (CLMM) can be implemented as an alternative for organisations to improve their Lean Manufacturing Management (LMM) processes. All members in the CLMM value chain must work together towards common objectives in order to make the LMM achievable in the collaborative environment. The novel research approach emphasises the use of Knowledge-Based (KB) approach in such activities as planning, designing, assessing and providing recommendations of CLMM implementation, through: a) developing the conceptual CLMM model; b) designing the KBCLMM System structure based on the conceptual model; and c) implementing Gauging Absences of Pre-requisites (GAP) analysis and Analytic Hierarchy Process (AHP) approach in the hybrid KBCLMM. The development of KBCLMM Model is the most detailed part in the research process and consists of five major components in two stages. Stage 1 (Planning stage) consists of Organisation Environment, Collaborative Business and Lean Manufacturing components. Stage 2 (Design stage) consists of Organisation CLMM Capability and Organisation CLMM Alignment components. Each of these components consists of sub-components and activities that represent particular issues in the CLMM development. From the conceptual model, all components were transformed into the KBCLMM System structure, which is embedded with the GAP and AHP techniques, and thus, key areas of potential improvement in the LMM are identified for each activity along with the identification of both qualitative and quantitative aspects for CLMM implementation. In order to address the real situation of CLMM operation, the research validation was conducted for an automotive manufacturer's Lean Manufacturing Chain in Malaysia. Published case studies were also used to test several modules for their validity and reliability. This research concludes that the developed KBCLMM System is an appropriate Decision Support System tool to provide the opportunity for academics and industrialists from the fields of industrial engineering, information technology, and operation management to plan, design and implement LMM for a collaborative environment.
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Mohamed, N. M. Z. Nik. "The Development of a Hybrid Knowledge-Based System for Designing a Low Volume Automotive Manufacturing Environment. The Development of A Hybrid Knowledge-Based (KB)/Gauging Absences of Pre-Requisites (GAP)/Analytic Hierarchy Process (AHP) System for the Design and Implementation of a Low Volume Automotive Manufacturing (LVAM) Environment." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5515.
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The product development process for the automotive industry is normally complicated, lengthy, expensive, and risky. Hence, a study on a new concept for Low Volume Automotive Manufacturing (LVAM), used for niche car models manufacturing, is proposed to overcome this issue. The development of a hybrid Knowledge Based (KB) System, which is a blend of KB System, Gauging Absences of Pre-requisites (GAP), and Analytic Hierarchy Process (AHP) is proposed for LVAM research. The hybrid KB/GAP/AHP System identifies all potential elements of LVAM issues throughout the development of this system. The KB System used in the LVAM analyses the gap between the existing and the benchmark organisations for an effective implementation. The novelty and differences in the current research approach emphasises the use of Knowledge Based (KB) System in the planning and designing stages by suggesting recommendations of LVAM implementation, through: a) developing the conceptual LVAM model; b) designing the KBLVAM System structure based on the conceptual LVAM model; and c) embedding Gauging Absences of Pre-requisites (GAP) analysis and Analytic Hierarchy Process (AHP) approach in the hybrid KBLVAM System. The KBLVAM Model explores five major perspectives in two stages. Planning Stage (Stage 1) consists of Manufacturer Environment Perspective (Level 0), LVAM Manufacturer Business Perspective (Level 1), and LVAM Manufacturer Resource Perspective (Level 2). Design Stage (Stage 2) consists of LVAM Manufacturer Capability - Car Body Part Manufacturing Perspective (Level 3), LVAM Manufacturer Capability - Competitive Priorities Perspective (Level 4), and LVAM Manufacturer Capability - Lean Process Optimisation Perspective (Level 5). Each of these perspectives consists of modules and sub-modules that represent specific subjects in the LVAM development. Based on the conceptual LVAM model, all perspectives were transformed into the KBLVAM System structure, which is embedded with the GAP and AHP techniques, hence, key areas of potential improvement are recommended for each activity for LVAM implementation. In order to be able to address the real situation of LVAM environment, the research verification was conducted for two automotive manufacturers in Malaysia. Some published case studies were also used to check several modules for their validity and reliability. This research concludes that the developed KBLVAM System provides valuable decision making information and knowledge to assist LVAM practitioners to plan, design and implement LVAM in terms of business organisation, manufacturing aspects and practices.
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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.
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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.
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Pabolu, Venkata Krishna Rao. "DFM – Weldability analysis and system development." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Produktutveckling - Datorstödd konstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-29316.
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This thesis work is mainly focused on the processes involved in manufacturing of aircraft engine components. The processes are especially about welding and welding methods. The basics of welding and the thesis support has been taken from the GKN Aerospace Sweden AB, a global aerospace product supplier. The basic objective of this thesis work is to improve the usability of an automation system which is developed for evaluating the weldability of a part. A long run maintainability aspect of this automation system has been considered. The thesis work addresses the problems arising during the usage of a computerised automated system such as process transparency, recognisability, details traceability and other maintenance aspects such as maintainability and upgradability of the system in the course of time. The action research methodology has been used to address these problems. Different approaches have been tried to finding the solution to those problems. A rule based manufacturability analysis system has been attempted to analyse the weldability of a component in terms of different welding technics. The software “Howtomation” has been used to improve the transparency of this analysis system. User recognisability and details tractability have been taken into account during the usage of a ruled based analysis system. The system attributes such as maintainability, upgradability, adaptiveness to modern welding methods has been addressed. The system suitability for large scale analysis has been considered.
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Aggarwal, Shubhesh, and Kjzal Kaldi. "Agile Project Management for Knowledge-Based Projects in Manufacturing Industry : Case Study: Epiroc Drilling Tools, Fagersta, Sweden." Thesis, Uppsala universitet, Industriell teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355239.
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Epiroc Drilling Tools is a manufacturing company that produces tools for rock mining and excavation. The company adopted the principles and framework of Lean Product Development in their R&D department with few practices of an agile framework called Scrum. These agile practices are used in the pre-study phase or the knowledge value stream of their lean product development. Hence, this research is limited to the knowledge value stream within the R&D department. The use of agile project management in manufacturing industry is unique and majority of the agile frameworks are specifically designed to suit the needs of software development companies. Several theories like Scrum, Lean, Kanban and DSDM were studied by the researchers to scrutinize the current framework of the department. The challenges and the similarities of the currently used framework with several other agile frameworks and the companies are discussed. Several qualitative research methods were adopted to know the viewpoints of the working employees in the department which are compared with other companies like Volvo Cars, ABB, LShift, EnergySoftware and from another division of Epiroc called Rocktec Automation who faced some similar challenges while practicing agile project management. After further research on the theories and comparison of the process, roles of the working employees and documentations within the knowledge value stream, DSDM had more similarities with the currently used framework than Scrum. This allowed to recommend ways that can fill the missing gaps using practices of DSDM without altering the existing working procedure in the knowledge value stream. This ensures that the improvement in the knowledge value stream remains continuous. On the contrary, a brief discussion is included on whether there is a need to be agile for manufacturing industries or is it just a changing trend in the field of project management.
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Achanga, Pius Coxwell. "Development of an impact assessment framework for lean manufacturing within SMEs." Thesis, Cranfield University, 2007. http://dspace.lib.cranfield.ac.uk/handle/1826/3521.
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The main aim of the research work presented in this thesis, is the development of a novel framework with the capability of assessing the impact of implementing lean manufacturing within small-to-medium sized manufacturing firms (SMEs). By assessing the impact of lean implementation, SMEs can make informed decisions on the viability of lean adoption at the conceptual implementation stage. Companies are also able determine their status in terms of lean manufacturing affordability. Thus, in order to achieve the above-stated aim, the following were the main set research objectives; (1) identifying the key drivers for implementing lean manufacturing within SMEs, (2) investigating the operational activities of SMEs in order to understand their manufacturing issues, (3) exploring the current level of lean manufacturing usage within SMEs so as to categorise users based on their levels of involvement, (4) identifying factors that determine the assessment of lean manufacturing, (5) developing an impact assessment framework for justifying lean manufacturing within SMEs, (6) developing a knowledge based advisory system and (7) validating the impact assessment framework and the developed knowledge based advisory system through real-life case studies, workshops, and expert opinions. A combination of research methodology approaches have been employed in this research study. This comprises literature review, observation of companies' practices and personal interview. The data collection process involved ten SMEs that provided consistent information throughout the research project life. Additionally, visitations to three large size manufacturing firms were also conducted. Hence, the framework and system development process passed through several stages. Firstly, the data were collected from companies who had successfully implemented lean manufacturing within their premise. The second development stage included the analysis and validation of the dataset through company practitioners. An impact assessment framework was thus developed with the aid of regression analysis as a predictive model. However, it was realised that there were few correlations between the dataset generated and analysis. The reasons for this were unclear. ,a knowledge based advisory system was adopted to conceptualise, enhance the robustness of the impact assessment framework and address the problem of the imprecise data in the impact assessment process. Three major factors of impact assessment were considered in the framework and the system development process, namely relative cost of lean implementation, a company lean readiness status and the level of value-added to be achieved (impact/benefits). Three knowledge based advisory sub-systems that consisted of the abovementioned factors were built. Results obtained from them were then fed into the final system. The three sub-systems were validated with the original set of data from companies. This enabled the assignment of a number of input variables whose membership functions aided the definition of the fuzzy expert system language (linguistic variables) used. The final system yielded heuristic rules that enable the postulation of scenarios of lean implementation. Results were sought and tested on a number of firms based within the UK, for the purposes validation. These also included expert opinions both in academic and industrial settings. A major contribution of the developed system is its ability to aid decision-making processes for lean implementation at the early implementation stage. The visualisation facility of the developed system is also useful in enabling potential lean users to make forecasts on the relative cost of lean projects upfront, anticipate lean benefits, and realise one' degree of lean readiness.
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Milana, Milana. "The Development of a Hybrid Knowledge-Based System for Integrated Maintenance Strategy and Operations in an Automotive Industry Environment: The Development of a Hybrid Knowledge-Based (KB) System/ Gauging Absences of Pre-Requisites (GAP)/Analytic Hierarchy Process (AHP) Methodology for Integrated Maintenance Strategy and Operations in an Automotive Industry Environment." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17446.
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The dependency of maintenance as a manufacturing logistic function has made
the considerations of maintenance decisions complex in nature. The importance
of maintenance has escalated significantly by the increasing of automation in
manufacturing processes. This condition switches the traditional maintenance
perspective of “fire-fighter” into the business competitive driver. As a
consequence, maintenance needs to consider other related aspects of decision
making to achieve competitive advantage.
This research aims to develop a hybrid Knowledge-Based (KB)
System/GAP/AHP methodology to support the integration of maintenance
decision with business and manufacturing perspectives. It constructs over 2000
KB rules on Strategic Stage (business and manufacturing aspects) and
Maintenance Operations Stage (maintenance aspects). Each aspect contains
KB rules attached with GAP analysis to assess the gap between current and
prerequisite condition. AHP analysis is then deployed to compare those aspects
structurally in a pair-wise manner to identify the critical ones to be rectified. This
hybrid KB system is useful in reviewing the existing maintenance system
performance and provides reasonable recommendations to improve
maintenance performance with respect to business and manufacturing
perspectives. Eventually, it indicates the roadmap from the current state to the
benchmark goals for the maintenance system.
This novel methodology of KBS/GAP/AHP to support maintenance decision is
developed for a particular application in the automotive environment. The
validation is conducted in two automotive companies in Indonesia and one
published case study in an automotive company. The result confirms that the
developed KB system can provide the valid, reasonable and consistent result to
propose structured recommendation for maintenance improvement priority.
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Wang, Zhiping. "Constructive generative design methods for qualified additive manufacturing." Thesis, Ecole centrale de Nantes, 2021. https://tel.archives-ouvertes.fr/tel-03670417.
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Les technologies de fabrication additive (FA) donnent de plus en plus de liberté de conception aux concepteurs et aux ingénieurs pour concevoir et définir des géométries et des compositions de matériaux très complexes. En raison d'un traitement couche par couche, les contraintes, méthodes, outils et processus de conception en FA sont différents de ceux des processus de fabrication traditionnels. Les méthodes et outils de conception traditionnels ne peuvent pas répondre aux besoins de la conception en FA. Par conséquent, un nouveau domaine de recherche, la conception pour la FA (Design for AM - DfAM), a émergé pour répondre à ce besoin. Cependant, les méthodes de DfAM existantes sont soit des lignes directrices, soit des outils de calculs, qui ont une prise en compte limitée des contraintes couplées le long de la chaîne de traitement numérique de la FA et peinent à garantir la fabricabilité de la conception en FA. Pour contribuer à l’obtention d’une conception qualifiée en FA, ce travail de thèse se concentre sur trois problèmes existants typiques dans le domaine du DfAM : premièrement, com-ment assurer la fabricabilité dans le processus d’optimisation topologique ? Deuxièmement, comment concevoir des structures de supports allégées, faciles à retirer pour le post-traitement et de diffusion de chaleur conviviales pour assurer la précision de la forme et améliorer la rugosité de surface des pièces imprimées ? Enfin, comment éviter les pertes de précision lors de la préparation de l'impression de structures en treillis complexes et assurer leur fabricabilité lors de la conception ?Pour résoudre les trois problèmes identifiés, ce travail de thèse propose un ensemble de nouvelles méthodes de conception générative constructive : 1. Méthode de conception générative basée sur un modèle CSG pour assurer la fabricabilité dans l'optimisation de la topologie de la structure allégée ; 2. Méthode de conception générative constructive basée sur des modèles pour optimiser la conception de la structure de supports et 3. Conception constructive inversée basée sur les « parcours d'outils » pour obtenir directement des modèles de traitement de structures poreuses ou de réseaux complexes correspondants avec des « parcours d'outils » pour obtenir directement des modèles de traitement de structures poreuses ou de réseaux complexes correspondants avec des « parcours d'outils » d'impression qualifiés. Les trois méthodes proposées intègrent les contraintes de processus de FA, réalisent un contrôle paramétrique et économisent des coûts de calcul dans le processus de conception pour obtenir un ensemble de solutions de conception candidates avec une fabrication garantie. Un ensemble d'études comparatives avec les méthodes DfAM existantes et quelques études de cas expérimentaux dans des applications médicales ont démontré les avantages des méthodes proposées. Ces méthodes constructives peuvent avoir un grand potentiel d'application pour être adoptées comme outils de conception et de prise de décision pour d'autres applications industrielles lorsqu'un DfAM qualifié est requisAdditive manufacturing (AM) technologies give more and more design freedom to designers and engi-neers to design and define highly complex geometries and material compositions. Due to a layer-by-layer processing, the constraints, methods, tools and processes of design in AM are different from that in traditional manufacturing processes. Traditional design methods and tools cannot meet the needs of design in AM. Therefore, a new re-search field, design for AM (DfAM), has emerged to serve this need. However, existing DfAM methods are either guidelines or pure computation-based, which have limited consideration of coupled constraints along the AM digital processing chain and are difficult to ensure manufactura-bility of design in AM. To obtain qualified design in AM, this research focuses on three typical existing problems in DfAM domain: Firstly, how to ensure manufacturability in (topology optimization) TO process? Secondly, how to design support structures with lightweight, easy-to-remove for post-processing and friendly heat-diffusion properties to ensure shape accuracy and improve surface roughness of printed parts? Finally, how to avoid accuracy loss in print-ing preparation of complex lattice structures and ensure their manufacturability in design?To solve the three identified problems, this research developed a set of new constructive genera-tive design methods: 1. CSG-based generative design method to ensure manufacturability in light-weight topology optimization; 2. Pattern-based constructive generative design method to optimize support structure design and 3. Toolpath-based inversed constructive design to directly ob-tain processing models of corresponding complex lattice or porous structures with qualified print-ing toolpaths. The three proposed methods can well embed AM process constraints, realize para-metric control and save computation cost in design process to obtain a set of candidate design solutions with ensured manufacturability. A set of comparison studies with existing DfAM meth-ods and a couple of experiment case studies in medical applications demonstrated the methods’ advantages. These constructive methods may have large application potential to be adopted as design and decision making tools for other industrial applications when qualified DfAM is required
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Butdee, Suthep. "Development of a hybrid intelligent process planning system for rotational parts." Thesis, Queensland University of Technology, 1997.
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Thorn, Jessica Paula Rose. "Ecosystem services, biodiversity and human wellbeing along climatic gradients in smallholder agro-ecosystems in the Terai Plains of Nepal and northern Ghana." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:3319dafc-5b0c-436a-b653-a623fc3e8de4.
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Increasingly unpredictable, extreme and erratic rainfall with higher temperatures threatens to undermine the adaptive capacity of food systems and ecological resilience of smallholder landscapes. Despite growing concern, land managers still lack quantitative techniques to collect empirical data about the potential impact of climatic variability and change. This thesis aims to assess how ecosystem services and function and how this links with biodiversity and human wellbeing in smallholder agro-ecosystems in a changing climate. To this end, rather than relying on scenarios or probabilistic modelling, space was used as a proxy for time to compare states in disparate climatic conditions. Furthermore, an integrated methodological framework to assess ecosystem services at the field and landscape level was developed and operationalised, the results of which can be modelled with measures of wellbeing. Various multidisciplinary analytical tools were utilised, including ecological and socio-economic surveys, biological assessments, participatory open enquiry, and documenting ethnobotanical knowledge. The study was located within monsoon rice farms in the Terai Plains of Nepal, and dry season vegetable farms in Northern Ghana. Sites were selected that are climatically and culturally diverse to enable comparative analysis, with application to broad areas of adaptive planning. The linkages that bring about biophysical and human changes are complex and operate through social, political, economic and demographic drivers, making attribution extremely challenging. Nevertheless, it was demonstrated that within hotter and drier conditions in Ghana long-tongued pollinators and granivores, important for decomposition processes and pollination services, are more abundant in farms. Results further indicated that in cooler and drier conditions in Nepal, the taxonomic diversity of indigenous and close relative plant species growing in and around farms, important for the provisioning of ecosystem services, decreases. All other things equal, in both Nepal and Ghana findings indicate that overall human wellbeing may be adversely effected in hotter conditions, with a potentially significantly lower yields, fewer months of the year in which food is available, higher exposure to natural hazards and crop loss, unemployment, and psychological anxiety. Yet, surveys indicate smallholders continue to maintain a fair diversity of species in and around farms, which may allow them to secure basic necessities from provisioning ecosystem services. Moreover, farmers may employ adaptive strategies such as pooling labour and food sharing more frequently, and may have greater access to communication, technology, and infrastructure. Novel methodological and empirical contributions of this research offer predictive insights that could inform innovations in climate-smart agricultural practice and planning.
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Madhusudanan, N. "Acquiring diagnostic knowledge from documents to predict issues in aircraft assembly." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5345.
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Expert knowledge is important in a product's lifecycle, especially during the manufacturing
part of the lifecycle. Most of such knowledge is obtained through experience and its reuse can
help prevent potential issues in subsequent product development. Extracting the knowledge
acquired during one development cycle for reuse in subsequent development closes the knowledge
loop within a product's lifecycle. This thesis is aimed at acquiring expert knowledge from the
manufacturing and assembly phases in aerospace manufacturing for eventual use in the planning
and design stages.
Given the difficulties in knowledge acquisition from experts, this thesis focuses on acquiring
expert knowledge from text documents. The proposed method consists of three parts -
segregation of relevant text, acquisition of issues, causes and parameters, and realising context
of knowledge. These parts become the research questions to be addressed in this thesis.
The segregation of relevant text involves identifying coherent segments and then classifying
the relevant segments. The method proposed for segregation is based on discourse representation
that treats documents as a discourse, and attempts to measure topic changes by looking
at the relatedness between the discourse entities. A measure for calculating similarity between
sentences is used for identifying segments. Implementation and validation of the method with
human subjects is described.
The acquisition of diagnostic knowledge is performed by first identifying the parts of text
containing issues. Functional modelling and sentiment analysis are considered, and the latter
is chosen. A strategy for finding the causes of issues based on text patterns and sentiment is
proposed.
Once the issues and causes are known, the relations and parameters that form the cause are
dissected to represent the knowledge as rules in a knowledge base. A hybrid method using both
text patterns and dependency parse of the text representing cause is proposed. The knowledge
acquisition pipeline from the issues and causes to the rules is implemented. The acquisition of causes and effects is also cross-validated with human subjects.
Once the knowledge is acquired, methods for capturing the context (in which it was acquired
or it needs to be applied) are proposed. Context containers that make use of proximity
of words to ve factors defined to influence assemblability are used to capture the context. It
is expected that assembly situations can be described using these, and enable to match the
knowledge to a similar situation when reusing the knowledge. At the application stage, an
assembly situation model is proposed that combines product and process information to model
the application situation. The application of knowledge and implementation of these models is
part of a legacy-knowledge based smart manufacturing system under development.
This thesis concludes with a discussion of how the objectives were met, how the current
methods could be improved and the directions in which the methods could be extended.
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Wang, Peng. "A smart experience-based knowledge analysis system (SEKAS)." Thesis, 2014. http://hdl.handle.net/1959.13/1054153.
Full textAbstract:
Research Doctorate - Doctor of Philosophy (PhD)This thesis addresses issues associated with using ever-increasing amounts of information and knowledge more effectively, and taking advantage of knowledge generated through experience. With very fast expansion of the Internet has created several problems and challenges linked to the increasing amount of information in Web content. These challenges are related mainly to the difficulty of extracting potentially useful information and knowledge from Internet pages. Data mining is a tool that enables enterprises to learn from existing experience by providing them with useful and accurate trends about their customers’ behaviour, and assists organisations in predicting which products their customers may be interested in buying. Moreover, in the real world, it is common to face optimisation problems that have two or more objectives that must be optimised at the same time, that are typically explained in different units, and are in conflict with one another. The evolutionary algorithm can use experience that is derived from a former decision event to improve the evolutionary algorithm’s ability to find optimisation solutions rapidly and efficiently. A hybrid structure, the Smart Experience-based Knowledge Analysis System (SEKAS), is put forward in this thesis to address issues of knowledge management and use. SEKAS combines a set of experience knowledge structures (SOEKS) with multiple techniques to provide a comprehensive knowledge management approach capturing, discovering, reusing and storing knowledge for the users. The SEKAS integrates a novel Decisional DNA (DDNA) knowledge structure with the traditional web crawler technologies. DDNA, as a knowledge representation platform, can help deal with noisy and incomplete data, with learning from experience, and with making precise decisions and predictions in vague and fuzzy environments. This thesis outlines the investigation of the combination of DDNA and feature selection algorithms to guarantee the future performance for prediction. The proposed approaches are general and extensible in terms of both designing novel algorithms, and in the application to other domains. The SEKAS integrates the evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II) (Deb et al. 2002), using experience that is derived from a former decision event, to improve the evolutionary algorithm’s ability to find optimal solutions rapidly and efficiently. The SEKAS application to solve a travelling salesman's problem shows that this new proposed hybrid model can find optimal, or close to true, Pareto-optimal solutions in a fast and efficient way. Several conceptual elements for this thesis have been implemented in the testing prototype, and the experimental results that were obtained show that the SEKAS system has great potential for managing knowledge, as well as improving the response times for providing accurate solutions. Consequently, the SEKAS can provide a universal knowledge management platform for mass autonomous mechanisms and provides many functionalities for improving the efficiency in the organisational decision-making process. A real-world implementation in clinical domain is also provided in this thesis. Clinical decisional events are acquired and formalised inside the system by using the experiential knowledge representation techniques SOEKS and Decisional DNA. Three different algorithms are then applied to the clinical experience, to provide a weighting of the different decision criteria, their fine-tuning, and the formalisation of new ones.
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Mpofu, Khumbulani. "Knowledge-based design of reconfigurable manufacturing system advisor." 2010. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000249.
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D. Tech. Mechanical Engineering.Describes reconfigurable manufacturing (RM) is a paradigm that promises to meet the turbulent demands in current global manufacturing. The major findings of this thesis are as follows; 1. The functional description of the machine tool provides a handy mechanism of aiding COTS machine builders come up with vary configurations of machine tools and their classification from a predefined set of COTS modules. 2. The process of linking the respective part demands to the relevant COTS RMT is a rigorous and tiresome process that demands computational power provided for by the KBS. 3. The subjective linguistic manner of linking the parts and the machine configuration can be managed by including an objective constraint for the fuzzy model. 4. Coupling the decision making using a mathematical model with the use of a KBS brings about the optimum route to arriving to the desired configuration.
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Liu, Ming-Fang, and 劉明芳. "A Knowledge-based Scheduling of a Flexible Manufacturing System." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/10532120541676893728.
Full textAbstract:
碩士國立成功大學
航空太空工程學系
84
The FMS scheduling problem concerns the flow of parts and determining the sequence of operations at each machine tool. In this thesis, a heuristic scheduling method is proposed and it combines various dispatching rules in response to the dynamic status of the system. Results show that dispatching rules have a large impact on different system performance measures, such as the average machine utilization.Consequently,according to a criterion, the scheduling mechanism evaluates dispatching rules and selects the best dispatching rule for that criterion. Meanwhile, the system requires an immediate response to some external events,such as urgent parts, machine maintenance and machine breakdown, which may happen during the processing of production. To handle this situation, a new scheduling is performed with the remaining operations in the simulation mechanism to select a new rule. Object-oriented modeling and programming, which has been proposed as a solution to the reusable software development, is used to design the system modules.Finally,system integration is discussed and the role of scheduling is also described.
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Xu, Chun-Lai, and 許春來. "A study for knowledge-based computer integrated manufacturing system." Thesis, 1987. http://ndltd.ncl.edu.tw/handle/96825503580027469690.
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Ahmed, Muhammad Bilal. "Smart virtual product development system." Thesis, 2021. http://hdl.handle.net/1959.13/1420676.
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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.
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Po-ShengTseng and 曾柏盛. "Development of a Knowledge-based System for Materials Additive Manufacturing." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/15959099279624200602.
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碩士國立成功大學
材料科學及工程學系
103
There are new knowledges generated in the field of additive manufacturing (AM) technologies. To extract knowledge effectively, we need a knowledge-based system with a clear structure. There are three aspects of AM technologies, including classification of processings, materials and application of products. A knowledge-based system has been established from the standpoint of knowledge management and demonstrated in the form of websites. The knowledge-based system is separated to three divisions, which are classification of processings, materials and application of products. In the first division, processings are classified into seven categories based on phases of starting materials. Phases of starting materials involve liquid phase, semi-liquid phase and solid phase. AM technologies based on liquid phase include vat photo-polymerization, material jetting and binder jetting . AM technology based on semi-liquid phase is termed as fused deposition modeling . AM technologies based on solid powders include powder bed fusion and direct energy deposition. AM technologies based on solid sheets is known as sheet lamination . In the second division, the materials are divide into three categories, which are polymers, metals and ceramics. In the third division, applications of products are illustrated by case studies. Three divisions are presented in the form of a clear-structured website . The knowledge-based system for additive manufacturing is valuable for knowledge management. Comparing with other methods of knowledge acquisition, the knowledge-based system is easier to be updated , therefore it is an excellent medium for popularisation the information related to AM technologies.
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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.
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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.
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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.
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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.
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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.
Full textAbstract:
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.
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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.
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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.
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Mohamed, N. M. Z. Nik, and M. Khurshid Khan. "The development of a hybrid knowledge-based system for the design of a Low Volume Automotive Manufacturing (LVAM) system." 2012. http://hdl.handle.net/10454/9524.
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NoA conceptual design approach is an important stage for the development of a hybrid Knowledge-Based System (KBS) for Low Volume Automotive Manufacturing (LVAM). The development of a hybrid KBS, which is a blend of KBS and Gauging Absences of Pre-requisites (GAP), is proposed for LVAM research. The hybrid KB/GAP system identifies all potential elements of LVAM issues throughout the development of this system. The KBS used in the system design stage of the LVAM system analyses the gap between the existing and the benchmark organisations for an effective implementation through the GAP analysis technique. The proposed KBLVAM model at the design stage explores three major components, namely LVAM car body parts manufacturing perspective, LVAM competitive priorities perspective and LVAM lean environment perspective. Initial results reveal that the KBLVAM system has identified, for each perspective modules and sub-modules, the Problem Categories (PC) in a prioritised manner.
The financial support by the Malaysian Government, Universiti Malaysia Pahang and University of Bradford for this research is grateful acknowledged.
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Lee, Yuan-Chen, and 李元鎮. "Design and Verification of Intelligent Mold-manufacturing Navigating System with Knowledge-based Management." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/41592466775695265669.
Full textAbstract:
碩士中原大學
機械工程研究所
96
With the flourishing development of 3C industry, the products get more diversified and the life cycle keeps reducing. Therefore, the mold development and the manufacturing time should be reduced as requested. Due to the shortage of professional engineers, the overall mold industry is unable to achieve the product specifications only by the accumulated experiences and knowledge. Nowadays, in order to maintain and enhance the competitiveness, the important goal of enterprise is to shorten the time of mold design and manufacturing, train new engineers in the shortest time, and upgrade the quality of products. The first part of this study is to develop a mold-manufacturing navigating system with knowledge management on the CAD/CAM software Pro/ENGINEER 3.0 Browser. The CAM engineers use the mold-manufacturing navigating system with process-planning navigating system to produce the machine codes of processing machine. The historical database and technological database of the system are used to prevent the unnecessary mistakes caused by the lack of experience. Since Pro/ENGINEER and UG are the most commonly used CAD/CAM software, the second part of this study is to migrate the mold-manufacturing navigating system with knowledge management to the CAD/CAM software UG NX5 Browser, and compare the capability and practicality. This study used cell-phone core and cavity as examples. The time and benefits of using and without-using mold-manufacturing navigating system is compared. The results show that using the mold-manufacturing navigating system can shorten the time of NC programming from 60~120 minutes to 30~60 minutes, and it can also save time at wire-cutting programming and electrode designing. The assistance of historical database and technological database can not only reduce the mistakes of machine-path planning, but also increase the rate of tool usage.
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Mohamed, N. M. Z. Nik, and M. Khurshid Khan. "Knowledge based system implementation for lean process in low volume automotive manufacturing (LVAM) with reference to process manufacturing." 2011. http://hdl.handle.net/10454/9541.
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YesGlobal manufacturing industry mostly depends on new product development and processes to become competitive. The product development process for automotive industry is normally complicated, lengthy, expensive, and risky. Hence, a study of lean manufacturing processes for low volume manufacturing in automotive industry is proposed to overcome this issue by eliminating all wastes in the lengthy process. This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for lean process in Low Volume Automotive Manufacturing (LVAM). The research concentrates on the low volume processes by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potential waste elements of low volume process manufacturing. The KB system analyses the difference between the existing and the benchmark standards for lean process for an effective implementation through the GAP analysis technique. The proposed model explores three major lean process components, namely Employee Involvement, Waste Elimination, and Kaizen (continuous improvement). These three components provide valuable information in order for decision makers to design and implement an optimised low volume manufacturing process, but which can be applied in all process manufacturing, including chemical processing.
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Khan, M. Khurshid, I. Hussain, and S. Noor. "A knowledge based methodology for planning and designing of a flexible manufacturing system (FMS)." 2011. http://hdl.handle.net/10454/9521.
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NoThis paper presents a Knowledge-Based (KB) integrated approach for planning and designing of number of machining centres, selection of material handling system, layout and networking architecture and cost analysis for a Flexible Manufacturing Systems (FMS). The KB model can be applied for integrating the decision issues at both the planning and designing stages of an FMS for three types of layouts (single row, double row, and loop) and three MHS types (robot-conveyor, AGV-conveyor and a hybrid AGV-robot-conveyor). The KB methodology starts from a suitable information input, which includes demand per year of part types, part type’s information, machining centre’s calculation, Material Handling System (MHS) selection, machining centre’s layout selection, networking selection and financial analysis. The KB methodology is developed by using AM, an expert system shell, and contains over 1500 KB rules. The performance of the system has been verified and validated through four published and four industrial case studies, respectively. The validation results from industry show that the KB methodology is capable of considering detailed design inputs and is able to assist in designing and selecting a practical FMS. It is concluded that a KB system for the present FMS application is a viable and efficient methodology.
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Milana, M., M. Khurshid Khan, and J. Eduardo Munive-Hernandez. "A framework of Knowledge Based System for Integrated Maintenance Strategy and Operation." 2014. http://hdl.handle.net/10454/8767.
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NoThe dependency of maintenance as a manufacturing logistic function has made the considerations and constrains of maintenance decisions complex in nature. The rapid growth of automation in manufacturing process has also increased the role of maintenance as an inseparable business partner. As consequence, maintenance strategy and operations should always be aligned with business and manufacturing perspectives within a holistic and integrated manner to achieve competitive advantage. This paper presents a framework of Knowledge Based System for Integrated Maintenance Strategy and Operation (KBIMSO) linked to business and manufacturing perspectives. The KBIMSO framework has novelty of simultaneously highlighting the elements of business, manufacturing and maintenance perspectives which contribute to direct maintenance performance and can be used by the companies to evaluate their existing maintenance system in relation to business competitive priorities and manufacturing process requirements in order to gain optimal maintenance performance as the competitive driver.
Support for this study is provided by the Directorate of Higher Education, Ministry of National Education, Republic of Indonesia and the University of Bradford, the United Kingdom.
The full text cannot be displayed due to the publisher's copyright agreement.
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Milana, M., M. Khurshid Khan, and J. Eduardo Munive-Hernandez. "Design and development of Knowledge Based System for Integrated Maintenance Strategy and Operations." 2016. http://hdl.handle.net/10454/16898.
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YesThe importance of maintenance has escalated significantly by the increase in automation in manufacturing processes. This condition changed the perspective of maintenance from being considered as an inevitable cost to being seen as a key business function to drive competitiveness. Consequently, maintenance decisions need to be aligned with the business competitive strategy as well as the requirements of manufacturing/quality functions in order to support manufacturing equipment performance. Therefore, it is required to synchronise the maintenance strategy and operations with business and manufacturing/quality aspects. This article presents the design and development of a Knowledge Based System for Integrated Maintenance Strategy and Operations. The developed framework of the Knowledge Based System for Integrated Maintenance Strategy and Operations is elaborated to show how the Knowledge Based System for Integrated Maintenance Strategy and Operations can be applied to support maintenance decisions. The knowledge-based system integrates the Gauging Absences of Prerequisites methodology in order to deal with different decision-making priorities and to facilitate benchmarking with a target performance state. This is a new contribution to this area. The Knowledge Based System for Integrated Maintenance Strategy and Operations is useful in reviewing the existing maintenance system and provides reasonable recommendations for maintenance decisions with respect to business and manufacturing perspectives. In addition, it indicates the roadmap from the current state to the benchmark goals for the maintenance system.
Ministry of Research, Technology and Higher Education of the Republic of Indonesia and the University of Bradford, UK.
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Peres, Ricardo Alexandre Fernandes da Silva. "An agent based architecture to support monitoring in plug and produce manufacturing systems using knowledge extraction." Master's thesis, 2015. http://hdl.handle.net/10362/16562.
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In recent years a set of production paradigms were proposed in order to capacitate manufacturers to meet the new market requirements, such as the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production standardized consumables.
These new paradigms advocate solutions capable of facing these requirements, empowering manufacturing systems with a high capacity to adapt along with elevated flexibility and robustness in order to deal with disturbances, like unexpected orders or malfunctions.
Evolvable Production Systems propose a solution based on the usage of modularity and self-organization with a fine granularity level, supporting pluggability and in this way allowing companies to add and/or remove components during execution without any extra re-programming effort.
However, current monitoring software was not designed to fully support these characteristics, being commonly based on centralized SCADA systems, incapable of re-adapting during execution to the unexpected plugging/unplugging of devices nor changes in the entire system’s topology.
Considering these aspects, the work developed for this thesis encompasses a fully distributed agent-based architecture, capable of performing knowledge extraction at different levels of abstraction without sacrificing the capacity to add and/or remove monitoring entities, responsible for data extraction and analysis, during runtime.