A major issue within automotive Sheet Metal Forming (SMF) concerns ensuring desired output product quality and consistent process performance. This is fueled by complex physical phenomena, process fluctuations and complicated parameter correlations governing the dynamics of the production processes. The aim of the thesis is to provide a deeper understanding of the challenges and opportunities in this regard within automotive SMF. The research is conducted in collaboration with a global automotive manufacturer.  The research shows that systematic investigations using process simulation models allow exploration of the product-process parameter interdependencies and their influence on the output product quality. Furthermore, it is shown that incorporating in-line measured data within process simulation models enhance model prediction accuracy. In this regard, automating the data processing and model configuration tasks reduces the overall modelling effort. However, utilization of results from process simulations within a production line requires real-time computational performance. The research hence proposes the use of reduced process models derived from process simulations in combination with production data, i.e. a hybrid data- and model-based approach. Such a hybrid approach would benefit process performance by capturing the deviations present in the real process while also incorporating the enhanced process knowledge derived from process simulations. Bringing monitoring and control realms within the production process to interact synergistically would facilitate the realization of such a hybrid approach. The thesis presents a procedure for exploring the causal relationship between the product-process parameters and their influence on output product quality in addition to proposing an automated approach to process and configure in-line measured data for incorporation within process simulations. Furthermore, a framework for enhancing output product quality within automotive SMF is proposed. Based on the thesis findings, it can be concluded that in-line measured data combined with process simulations hold the potential to unveil the convoluted interplay of process parameters on the output product quality parameters. / <p>Related work:</p><p>1) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14412</p><p>2) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14388</p><p>3) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18935</p>

Nytt AB is a startup focusing on simplifying the concept of smart manufacturing to small and medium-sized companies providing an add-on machine monitoring solution for data analyzing. The product is currently under development with the final product soon to be launched. In the next phase of Nytt AB, a marketing plan has to be strategized. This thesis, which is built on previous Nytt AB’s work, focuses on addressing issues that will be important when creating a marketing and sales strategy. The customer is put as the focal point and the customer values analyzed including discussions on productivity improvements within the machines and how to standardize the changes to satisfy every customer. Using research questions as a base for the study, the customer values were analyzed by first understanding the main threats, weaknesses, strengths, and opportunities for the product and then analyzing data from an installed prototype and the improvements that can be achieved based on the data. The customers can, by using the product get statistical facts about their machines which can be the first step to understand the need for changes within their company. With future development, further customer values will appear, such as providing aid on how to improve KPIs such as availability. Providing this information to the customers will help them obtain a better insight into the field of smart manufacturing, the manufacturing of the future. / Nytt AB är ett nystartat företag som fokuserar på att förenkla konceptet smart tillverkning för små och medelstora företag och erbjuder en maskinövervakningslösning för dataanalys. Produkten är just nu under utveckling, slutprodukten planeras lanseras snart. Nästa steg för Nytt AB är att strukturera en marknadsplan. Detta examensarbete, som bygger på tidigare arbete i Nytt AB, fokuserar på frågor som kommer att vara viktiga när man skapar en marknadsförings- och försäljningsstrategi. Kunden blir i fokus när kundvärdena är analyserade inklusive i diskussioner om produktivitetsförbättringar för maskiner och hur man standardiserar förändringarna för att uppfylla kundens krav. Med hjälp av forskningsfrågor, analyseras kundvärdena genom att först förstå de viktigaste hoten, svagheterna, styrkorna och möjligheterna för produkten och sedan analyseras data från prototypen och de förbättringar som kan uppnås baserat på given data. Kunderna kan genom att använda produkten få statistik om sina maskiner vilket kan vara första steget att inse behovet av förändringar inom kundföretaget. Vid utveckling kommer ytterligare kundvärden att dyka upp, till exempel metoder på hur man förbättrar olika nyckeltal, såsom tillgänglighet. Att ge denna information till kunderna hjälper dem att få en bättre inblick i smart tillverkning, framtidens tillverkning.

As a result of digitalization, previously analog systems in the manufacturing industry have become digitalized, including the decision-making processes. Companies are, therefore,becoming more dependent on data for strategic decisions. However, because of the rapid development of digitalization, companies are left blindfolded in the path towards smarter manufacturing which often leads to unsuccessful technological implementations. Therefore, the thesis will explore this problem by asking: What are the required initiatives for successfully implementing digital data-driven decision-making to improve efficiency and sustainability by Swedish manufacturing companies? To answer the research questions, an exploratory multiple case study approach was conducted, where interviews with informants from the industry as well as researchers within the context of smarter manufacturing were made. The findings were then used to derive propositions which worked as the foundation of a conceptual model which functionality would be to illuminate the results in the form of a strategy map. Findings suggest that it is not always necessary for companies to implement technologies linked to large investments to enable digital data-driven decision-making. However, for those that do, there needs to be a clear organizational plan and agenda before executing theprojects since they otherwise often lead to insufficient results. That means, the technological aspects are often not the culprit in failed digital data-driven decision-makingprojects. Additional findings suggest that there are synergies connected to digital data-driven decision-making such as data-sharing possibilities that have the potential of becoming a major aspect within the context of sustainability and efficiency. / Som ett resultat av ökad digitalisering har analoga system i tillverkningsindustrin blivit digitaliserade, vilket inkluderar beslutsfattandet. Företag har därför börjat förlita sig alltmer på data för sina strategiska beslut. Men på grund av den snabba utveckling av digitalisering har tillverkningsföretagen lämnats utan klara riktlinjer för hur de bör gå tillväga för att implementera digitalt datadrivet beslutsfattande på ett effektivt men hållbart sätt. Avhandlingen kommer därför att undersöka detta problem genom att fråga: Vilka är de initiativ som krävs för att framgångsrikt implementera digital datadrivet beslutsfattande med målet att förbättra effektiviteten och hållbarheten hos svenska tillverkningsföretag? För att svara på forskningsfrågorna användes en undersökande metod med flerafallstudier, där intervjuer gjordes med informanter från industrin såväl som forskare inom ramen för smartare tillverkning. Resultaten användes sedan för att härleda förslag som därefter användes till konstruktionen av en konceptuell model vars huvuduppgift var att illustrera resultaten i form av en strategikarta. Slutsatserna pekar på att det inte alltid är nödvändigt för företag att implementera teknik kopplad till stora investeringar för att möjliggöra digitalt datadrivet beslutsfattande. Men för de som valt att implementera sådana system behövs en tydlig organisationsplan innan projekten genomförs eftersom de annars ofta leder till ofördelaktiga resultat. Detta tyder på att de tekniska aspekterna oftast inte är vad som orsakar misslyckade datadrivna beslutsprojekt. Dessutom tyder resultaten på att det finns synergier kopplade till digitalt datadrivet beslutsfattande, till exempel möjligheter att dela data som har potential att bli en viktig aspekt inom hållbarhet och effektivitet.

<p>Developed countries have built wealth and prosperity on the strength of their manufacturing sectors, with China’s success story of lifting 800 million people out of extreme poverty in 30 years a sterling and most recent example of how manufacturing-led industrialization can foster economic development. Sub-Saharan Africa, unfortunately, find itself today in a similar situation as China did in 1990, with over 50% of the world’s desperately poor 719 million people living in the region. But unlike China, Sub-Saharan Africa is faced with the additional challenge of overcoming poverty in a world with stricter constraints to global trade and climate change limitations to modern-day industrialization. Compounding the challenges further is the region's limited know-how and human capital — a consequence of years of underdevelopment, creating a classic chicken and egg dilemma where the lack of industrialization perpetuates the dearth of know-how and human capital, and vice versa.</p><p>Considering these challenges, we investigate how chemical manufacturing and what chemical manufacturing approaches can be leveraged to effectively drive industrialization and economic development in Sub-Saharan Africa. We propose chemicals manufacturing using prefabricated modules – which are constructed offsite in places with available human capital and transported to be assembled in places where they are needed – as a flexible and needed approach. However, Economy of Scale, which generally favors large-scale chemical manufacturing, poses as a major constraint to such modularization approach, especially given the presently small serviceable market sizes in Sub-Saharan Africa due to low purchasing power parity. We thus utilize mathematical modeling techniques to determine and establish scenarios for economic viability of the proposed approach, providing modeling frameworks and introducing measures for further studies in the process. We also provide and analyze exemplary flowsheets synthesized for a net-zero carbon emissions chemical manufacturing paradigm in the region.</p><p>This work concludes with a prefeasibility study of a chemical manufacturing project in Nigeria, as part of the author’s quest to build prefabricated modular plants across Africa. <i>Modular plants are attractive as they can be tuned to market demand of a developing market and region that needs them, putting less capital at risk.</i></p><p>This thesis is intended to be a vanguard of potential solutions to the complex challenges to industrialization in Sub-Saharan Africa. It endeavors to pave the way for addressing these issues through chemical manufacturing, offering valuable insights for sustainable progress.</p>

<p>  </p> <p>The integration of Industry 4.0 technologies such as cyber-physical systems, the internet of things, and artificial intelligence has revolutionized the traditional manufacturing systems, making them smart and digital. Maintenance, a critical component of manufacturing, has been incorporated with data-driven strategies such as prognostic and health management (PHM) to improve production efficiency and reliability. This is achieved by real-time sensing and AI-based modeling, which monitor the health condition of operational equipment for fault detection or failure prediction. The results generated by these models provide crucial support for decision-making processes in manufacturing, ranging from maintenance scheduling to production management.</p> <p>This research focuses on data-driven machine health models based on deep learning in manufacturing systems and explores three directions towards the practical implementation of PHM: model interpretation, model adaptability and robustness enhancement, and cost-benefit analysis of maintenance strategies. In terms of model interpretation, the RNN-LSTM-based model prediction on bearing health estimation was analyzed, and the relationship between the model input and output was investigated. The adoption of the LRP technique improved the explainability of the LSTM model beyond predictive maintenance applications. To enhance model adaptability and robustness, a Transformer-based method was developed for fault diagnosis and novel fault detection, which achieved superior performance compared to conventional fault classification AI-based models. The decision-making aspect of PHM was addressed by conducting a cost-benefit analysis on different maintenance strategies, which provided a new perspective for decision-makers in maintenance management.</p>

Detta är ett examensarbete inom Industriell teknik och produktionsunderhåll som handlar om kundkrav för service av mobilnät i stora industrier. De stora industrierna, som exempelvis Scania och Boliden, har fokus på att underlätta underhåll och hantering av produktionen med hjälp av automation och digitalisering i sina företag. Idag är det nya mobilnätet, 5G, en efterfrågad teknologi i stora industrier med stora produktionsanläggningar, som gör det möjligt att koppla upp de fysiska komponenterna och därmed effektivisera sitt företag. I detta arbete har intervjuer genomförts med tre svenska företag, Ericsson, Scania och Boliden, där de stora kraven med implementering och användning av 5G tagits fram. Utöver den kvalitativ studien har en litteraturstudie genomförts, där kravet var att välja information som inriktar sig på 5G och dess implementering i stora produktionsanläggningar. Målet med detta arbete är att identifiera kundens behov av egen service och kundkrav på operatören vid implementering och användning av 5G, samt komma fram med ett förslag på ett SLA. Resultaten, från intervjuer och litteraturstudie, visar att kundens krav på operatören överväger kundkrav av egen service. Vid implementering av 5G måste industrikunder ställa om hela produktionen, vilket kräver ny utrustning, ny teknik och kompetent personal. Dessa krav ska operatörer, som implementerar och levererar mobilnät, ta hand om. Industrikunder har också krav att kunna själv hantera vissa delar i 5G-mobilnätet, exempelvis att ha tekniker som kan ta hand om enkel underhåll. Detta gör nätet säkrare och effektivare men även billigare, då operatören inte behöver genomföra enklar underhållsarbete. Vidare har ett förslag på ett SLA tagits fram, där vissa krav som industrikunder hade på ett serviceavtal dokumenterades. / The conducted project within the program of Industrial Technology and Production Maintenance concerns the industrial customer requirements for service of mobile networks within large industries. The large industrial customers, such as Scania and Boliden, have an immense focus on supporting the maintenance processes and managing production with the help of automation and digitalization within their companies. Today, the new mobile network of 5G, is a sought-after technology within large industries with large production facilities, that enables the opportunity to connect physical components and thereby increasing the efficiency within the operations. In this thesis work, interviews have been conducted with three Swedish companies, Ericsson, Scania, and Boliden. This is where the requirements of implementation and usage of 5G network have been determined. Moreover, a literature study has also been carried out with the requirement of selecting information that emphasizes on 5G and implementation within large production facilities. The objective of this thesis work is to identify the requirements of large industrial customers, such as their needs for self-maintenance, their requirements on the network operator during implementation and usage of 5G. Finally, the objective is to also present a Service Level Agreement or SLA. The result from the interview and the literature study shows that the customer requirements on the network operator are higher than the requirements the network operator has on thecustomers. During the implementation of 5G, industrial customers must adapt and change their production facilities, which requires new equipment, technology, and competent workers. These aspects should be considered by the and handled by the network operator. Industrial customers also have the condition of handling certain areas of the 5G network themselves, for example having technicians ready that can conduct simple maintenance. This in turn will enable more secure and efficient network and on the same time, at a lesser cost, due to the network operator no longer must conduct simple maintenance. Furthermore, a proposition for a Service Level Agreement have been composed, where the requirements from the industrial customers were documented.

In the era of Industry 4.0, the Internet-of-Things (IoT) plays the driving role comparable to steam power in the first industrial revolution. IoT provides the potential to combine machine-to-machine (M2M) interaction and real time data collection within the field of manufacturing. Therefore, the adoption of IoT in industry enhances dynamic optimization, control and data-driven decision making. However, the domain suffers due to interoperability issues, with massive numbers of IoT devices connecting to the internet despite the absence of communication standards upon. Heterogeneity is pervasive in IoT ranging from the low levels (device connectivity, network connectivity, communication protocols) to high levels (services, applications, and platforms). The project investigates the current state of industrial IoT (IIoT) ecosystem, to draw a comprehensive understanding on interoperability challenges and current solutions in supporting of IoT-based smart manufacturing. Based upon a literature review, IIoT interoperability issues were classified into four levels: technical, syntactical, semantic, and organizational level interoperability. Regarding each level of interoperability, the current solutions that addressing interoperability were grouped and analyzed. Nine reference architectures were compared in the context of supporting industrial interoperability. Based on the analysis, interoperability research trends and challenges were identified. FIWARE Generic Enablers (FIWARE GEs) were identified as a possible solution in supporting interoperability for manufacturing applications. FIWARE GEs were evaluated with a scenario-based Method for Evaluating Middleware Architectures (MEMS).  Nine key scenarios were identified in order to evaluate the interoperability attribute of FIWARE GEs. A smart manufacturing use case was prototyped and a test bed adopting FIWARE Orion Context Broker as its main component was designed. The evaluation shows that FIWARE GEs meet eight out of nine key scenarios’ requirements. These results show that FIWARE GEs have the ability to enhance industrial IoT interoperability for a smart manufacturing use case. The overall performance of FIWARE GEs was also evaluated from the perspectives of CPU usage, network traffic, and request execution time. Different request loads were simulated and tested in our testbed. The results show an acceptable performance in terms with a maximum CPU usage (on a Macbook Pro (2018) with a 2.3 GHz Intel Core i5 processor) of less than 25% with a load of 1000 devices, and an average execution time of less than 5 seconds for 500 devices to publish their measurements under the prototyped implementation. / I en tid präglad av Industry 4.0, Internet-of-things (IoT) spelar drivande roll jämförbar med ångkraft i den första industriella revolutionen. IoT ger potentialen att kombinera maskin-till-maskin (M2M) -interaktion och realtidsdatainsamling inom tillverkningsområdet. Därför förbättrar antagandet av IoT i branschen dynamisk optimering, kontroll och datadriven beslutsfattande. Domänen lider dock på grund av interoperabilitetsproblem, med enorma antal IoT-enheter som ansluter till internet trots avsaknaden av kommunikationsstandarder på. Heterogenitet är genomgripande i IoT som sträcker sig från de låga nivåerna (enhetskonnektivitet, nätverksanslutning, kommunikationsprotokoll) till höga nivåer (tjänster, applikationer och plattformar). Projektet undersöker det nuvarande tillståndet för det industriella IoT (IIoT) ekosystemet, för att få en omfattande förståelse för interoperabilitetsutmaningar och aktuella lösningar för att stödja IoT-baserad smart tillverkning. Baserat på en litteraturöversikt klassificerades IIoT-interoperabilitetsfrågor i fyra nivåer: teknisk, syntaktisk, semantisk och organisatorisk nivå interoperabilitet. När det gäller varje nivå av driftskompatibilitet grupperades och analyserades de nuvarande lösningarna för adressering av interoperabilitet. Nio referensarkitekturer jämfördes i samband med att stödja industriell driftskompatibilitet. Baserat på analysen identifierades interoperabilitetstrender och utmaningar. FIWARE Generic Enablers (FIWARE GEs) identifierades som en möjlig lösning för att stödja interoperabilitet för tillverkningstillämpningar. FIWARE GEs utvärderades med en scenariebaserad metod för utvärdering av Middleware Architectures (MEMS). Nio nyckelscenarier identifierades för att utvärdera interoperabilitetsattributet för FIWARE GEs. Ett smart tillverkningsfodral tillverkades med prototyper och en testbädd som antog FIWARE Orion Context Broker som huvudkomponent designades. Utvärderingen visar att FIWARE GE uppfyller åtta av nio krav på nyckelscenarier. Dessa resultat visar att FIWARE GE har förmågan att förbättra industriell IoT-interoperabilitet för ett smart tillverkningsfodral. FIWARE GEs totala prestanda utvärderades också utifrån perspektivet för CPU-användning, nätverkstrafik och begär exekveringstid. Olika förfrågningsbelastningar simulerades och testades i vår testbädd. Resultaten visar en acceptabel prestanda i termer av en maximal CPU-användning (på en Macbook Pro (2018) med en 2,3 GHz Intel Core i5-processor) på mindre än 25% med en belastning på 1000 enheter och en genomsnittlig körningstid på mindre än 5 sekunder för 500 enheter att publicera sina mätningar under den prototyperna implementateringen.

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

Tesis por compendio / [ES] El paradigma de la Industria 4.0 (I4.0) gravita en gran medida sobre el potencial de las tecnologías de la información y la comunicación (TIC) para mejorar la competitividad y sostenibilidad de las industrias. El concepto de Smart Manufacturing Scheduling (SMS) surge y se inspira de ese potencial. SMS, como estrategia de transformación digital, aspira a optimizar los procesos industriales mediante la aplicación de tecnologías como el gemelo digital o digital twin (DT), el modelo de gestión zero-defect manufacturing (ZDM), y el aprendizaje por refuerzo profundo o deep reinforcement learning (DRL), con el propósito final de orientar los procesos de programación de operaciones hacia una automatización adaptativa en tiempo real y una reducción de las perturbaciones en los sistemas de producción. SMS se basa en cuatro principios de diseño del espectro I4.0: automatización, autonomía, capacidad de acción en tiempo real e interoperabilidad. A partir de estos principios clave, SMS combina las capacidades de la tecnología DT para simular, analizar y predecir; la del modelo ZDM para prevenir perturbaciones en los sistemas de planificación y control de la producción; y la del enfoque de modelado DRL para mejorar la toma de decisiones en tiempo real. Este enfoque conjunto orienta los procesos de programación de operaciones hacia una mayor eficiencia y, con ello, hacia un mayor rendimiento y resiliencia del sistema productivo. Esta investigación emprende, en primer lugar, una revisión exhaustiva del estado del arte sobre SMS. Con la revisión efectuada como referencia, la investigación plantea un modelo conceptual de SMS como estrategia de transformación digital en el contexto del proceso de programación del taller de trabajos o job shop. Finalmente, la investigación propone un modelo basado en DRL para abordar la implementación de los elementos clave del modelo conceptual: el DT del taller de trabajos y el agente programador. Los algoritmos que integran este modelo se han programado en Python y han sido validados contra varias de las más conocidas reglas heurísticas de prioridad. El desarrollo del modelo y los algoritmos supone una contribución académica y gerencial en el área de la planificación y control de la producción. / [CA] El paradigma de la Indústria 4.0 (I4.0) gravita en gran mesura sobre el potencial de les tecnologies de la informació i la comunicació (TIC) per millorar la competitivitat i la sostenibilitat de les indústries. El concepte d'smart manufacturing scheduling (SMS) sorgeix i inspira a partir d'aquest potencial. SMS, com a estratègia de transformació digital, aspira a optimitzar els processos industrials mitjançant l'aplicació de tecnologies com el bessó digital o digital twin (DT), el model de gestió zero-defect manufacturing (ZDM), i l'aprenentatge per reforçament profund o deep reinforcement learning (DRL), amb el propòsit final dorientar els processos de programació doperacions cap a una automatització adaptativa en temps real i una reducció de les pertorbacions en els sistemes de producció. SMS es basa en quatre principis de disseny de l'espectre I4.0: automatització, autonomia, capacitat d¿acció en temps real i interoperabilitat. A partir d'aquests principis clau, SMS combina les capacitats de la tecnologia DT per simular, analitzar i predir; la del model ZDM per prevenir pertorbacions en els sistemes de planificació i control de la producció; i la de de l'enfocament de modelatge DRL per millorar la presa de decisions en temps real. Aquest enfocament conjunt orienta els processos de programació d'operacions cap a una eficiència més gran i, amb això, cap a un major rendiment i resiliència del sistema productiu. Aquesta investigació emprèn, en primer lloc, una exhaustiva revisió de l'estat de l'art sobre SMS. Amb la revisió efectuada com a referència, la investigació planteja un model conceptual de SMS com a estratègia de transformació digital en el context del procés de programació del taller de treballs o job shop. Finalment, la investigació proposa un model basat en DRL per abordar la implementació dels elements claus del model conceptual: el DT del taller de treballs i l'agent programador. Els algorismes que integren aquest model s'han programat a Python i han estat validats contra diverses de les més conegudes regles heurístiques de prioritat. El desenvolupament del model i els algorismes suposa una contribució a nivell acadèmic i gerencial a l'àrea de la planificació i control de la producció. / [EN] The Industry 4.0 (I4.0) paradigm relies, to a large extent, on the potential of information and communication technologies (ICT) to improve the competitiveness and sustainability of industries. The smart manufacturing scheduling (SMS) concept arises and draws inspiration from this potential. As a digital transformation strategy, SMS aims to optimise industrial processes through the application of technologies, such as the digital twin (DT), the zero-defect manufacturing (ZDM) management model and deep reinforcement learning (DRL), for the ultimate purpose of guiding operations scheduling processes towards real-time adaptive automation and to reduce disturbances in production systems. SMS is based on four design principles of the I4.0 spectrum: automation, autonomy, real-time capability and interoperability. Based on these key principles, SMS combines the capabilities of the DT technology to simulate, analyse and predict; with the ZDM model, to prevent disturbances in production planning and control systems; by the DRL modelling approach, to improve real-time decision making. This joint approach orients operations scheduling processes towards greater efficiency and, with it, a better performing and more resilient production system. This research firstly undertakes a comprehensive review of the state of the art on SMS. By taking the review as a reference, the research proposes a conceptual model of SMS as a digital transformation strategy in the job shop scheduling process context. Finally, it proposes a DRL-based model to address the implementation of the key elements of the conceptual model: the job shop DT and the scheduling agent. The algorithms that integrate this model have been programmed in Python and validated against several of the most well-known heuristic priority rules. The development of the model and algorithms is an academic and managerial contribution in the production planning and control area. / This thesis was developed with the support of the Research Centre on Production Management and Engineering (CIGIP) of the Universitat Politècnica de València and received funding from: the European Union H2020 programme under grant agreement No. 825631, “Zero Defect Manufacturing Platform (ZDMP)”; the European Union H2020 programme under grant agreement No. 872548, "Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS)"; the European Union H2020 programme under grant agreement No. 958205, “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)”; the European Union Horizon Europe programme under grant agreement No. 101057294, “AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability and Resilience” (AIDEAS); the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00, "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)"; the Valencian Regional Government, in turn funded from grant RTI2018- 101344-B-I00 by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, "Industrial Production and Logistics optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065); and the grant PDC2022-133957- I00, “Validation of transferable results of optimisation of zero-defect enabling production technologies for supply chain 4.0” (CADS4.0-II) funded by MCIN/AEI/10.13039/501100011033 and by European Union Next GenerationEU/PRTR. / Serrano Ruiz, JC. (2024). Job shop smart manufacturing scheduling by deep reinforcement learning for Industry 4.0 [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202871 / Compendio

<p>A thesis study investigating the development and evaluation of a computer vision (CV) system for a manufacturing assembly task is reported. The CV inference results are compared to a Manufacturing Process Plan and an automation method completes a buyoff in the software, Solumina. Research questions were created and three hypotheses were tested. A literature review was conducted recognizing little consensus of Industry 4.0 technology adoption in manufacturing industries. Furthermore, the literature review uncovered the need for additional research within the topic of CV. Specifically, literature points towards more research regarding the cognitive capabilities of CV in manufacturing. A CV system was developed and evaluated to test for 90% or greater confidence in part detection. A CV dataset was developed and the system was trained and validated with it. Dataset contextualization was leveraged and evaluated, as per literature. A CV system was trained from custom datasets, containing six classes of part. The pre-contextualization dataset and post-contextualization dataset was compared by a Two-Sample T-Test and statistical significance was noted for three classes. A python script was developed to compare as-assembled locations with as-defined positions of components, per the Manufacturing Process Plan. A comparison of yields test for CV-based True Positives (TPs) and human-based TPs was conducted with the system operating at a 2σ level. An automation method utilizing Microsoft Power Automate was developed to complete the cognitive functionality of the CV system testing, by completing a buyoff in the software, Solumina, if CV-based TPs were equal to or greater than human-based TPs.</p>