Customers today demand products of high quality, and industries must cope with issues related to that to stay competitive. Therefore, an endeavor to achieve zero defects and to work with zero defect manufacturing (ZDM) is common in industries today. ZDM aims to reduce the number of failures within a manufacturing process and thus only producing faultless products. Since defected items result in unexpected work, extra costs, claims and unsatisfied customers, it is important to avoid that in order to secure the company’s market share. Even though it implies challenges, companies must work with ZDM and quality tools to stay competitive. However, there is a gap between the literature of ZDM and how to accomplish ZDM in practice, which makes it hard for companies to apply the method. Hence, this thesis aims to address this gap and present how the human factors and quality contribute to the goal of zero defects. When working with a manually driven manufacturing setting, human factors must be considered as an important aspect. Mistakes will occur as long as humans work with the products, but the prerequisites for doing right must be as good as possible to be able to decrease the number of mistakes. Another factor to consider is the internal quality of different processes to ensure that customer demands are achieved through all stages. This study focused on finding suggestions for improvements towards zero defects in manual assembly and to present general improvement actions. The thesis is based on three main fields: ZDM, quality and human factors. The findings are connected both to literature searches made within these fields, but also through a case study at the focal company. In the analysis chapter, the reader is provided with information about how the specified problem areas are linked together and to the three main fields. By combining the literature search with a case study at a focal company, findings could be detected, collected and analyzed. Four areas could be identified in the analysis and highlighted in the discussion of the research questions. The highlighted areas were further used as a foundation to establish suggestion within the important areas. These acts as practical guidelines for how to reach zero defects in an existing production with the goal of minimizing the implementation gap of ZDM.

Foundry is an industry that is both very old and very forward-looking. It is an essential base for several industrial sectors in today’s world. However, the metal casting industry faces a lot of challenges, including quality. There is a need for a converging system that orients all the resources towards meeting the quality requirements throughout the whole process. Zero Defect Manufacturing (ZDM) is a concept that aims to meet this need by adopting the four strategies of Detection, Prevention, Prediction and Repair. In that context, ZDM has been a subject of several collaborations between industrial sectors and the scientific community given its importance to the industrial field. Nevertheless, there is a lack of research into the implementation of this concept in certain types of industries, such as foundries. This thesis aims to fill this gap by presenting a generic review and a better understanding of the implementation of the ZDM approach in foundries. A systematic literature study has been conducted and the result has been compared to the empirical data collected via interviews with managers working in the metal casting industry. It was found that the nine key enablers of ZDM implementation in foundries are 1) Sensors & embedded systems, 2) AI & DigitalTwins, 3) Advanced robotics, 4) Connectivity & mobility, 5) Cloud computing, 6) Edge computing, 7) Forecasting & Modelling, 8) Business Solutions, 9) Intelligence & control systems. Automation enhances ZDM in foundries by enabling continuous and consistent data collection throughout the whole process. Ensuring skilled labour in specific disciplines such as PLC programming and statistics is seen to be a major challenge for foundries. At the end of the thesis, propositions for future studies in the field of ZDM in foundries and the role of future technologies, such as metal 3D printing, are proposed.

The growing customer demands for product variety have put unprecedented pressure on the manufacturing companies. To maintain their competitiveness, manufacturing companies need to frequently and efficiently adapt their processes while providing high-quality products. Different advanced manufacturing technologies, such as industrial robotics, have seen a drastic usage increase. Nevertheless, traditional quality methods, such as quality inspection, suffer from significant limitations in highly customised small batch production. For quality inspection to remain fundamental for zero-defect manufacturing and Industry 4.0, an increase in flexibility, speed, availability and decision upon conformance reliability is needed. If robots could perform in-line quality inspection, defective components might be prevented from continuing to the next production stage. Recent developments in robot cognition and sensor systems have enabled the robot to carry out perception tasks they were previously unable to do. The purpose of this thesis is to explore the usage of robotic in-line quality inspection during changeable zero-defect manufacturing. To fulfil this aim, this thesis adopts a mixed-methods research approach to qualitative and quantitative studies, as well as theoretical and empirical ones. The foundation for this thesis is an extensive literature review and two case studies that have been performed in close collaboration with manufacturing companies to investigate how in-line quality inspection is perceived and utilised to enhance industrial robots. The empirical studies also aimed at identifying and describing what opportunities arise from having robotic in-line quality inspection systems. The result of this thesis is a synthesis of literature and empirical findings. From the literature review/study, the need for enhancing quality inspection was identified and a multi-layer quality inspection framework suitable for the digital transformation was proposed. The framework is built on the assumption that data (used and collected) needs to be validated, holistic, and online, i.e. when needed, for the system to effectively decide upon conformity to surpass the challenges of reliability, flexibility and autonomy. Empirical studies show that industrial robotic applications can be improved in precision and flexibility using the in-line quality inspection system as measurement-assisted. Nevertheless, this methodological changes and robot application face the hurdle of previous and current management decisions when passing from one industrial paradigm to another (e.g. mass production to flexible production). A discussion on equipment design and manufacturing process harmony and how in-line quality inspection and management can harmonise such a system was provided.

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