A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Integration of machine learning models in a microservices architecture
Achieving Zero Defect Manufacturing in the evolving landscape of Industry 4.0 requires advanced, scalable architectures that support proactive quality management and real-time defect detection. This thesis introduces a ZDM-focused microservices architecture designed to enhance modularity, resilience, and scalability within industrial manufacturing environments. By integrating Cyber-Physical Systems and Digital Twins, the proposed architecture facilitates continuous monitoring, dynamic data flow, and predictive analyt- ics, aligning with RAMI 4.0 standards to ensure seamless interoperability across systems. Emphasizing communication-driven design, the architecture leverages distributed microservices and specialized communication brokers to create a flexible, event-driven system. This enables efficient handling of high data volumes, real-time quality insights, and early anomaly detection. Through a structured evaluation of core architectural components, including orchestration, choreography, and communication brokers, this work establishes a foundation for ZDM implementations adaptable to various industrial settings. The architecture’s deployment in a real-world manufacturing case demonstrates its practical benefits, illustrating how modular, scalable systems can drive operational improvements and defect reduction. Contributing to the broader Industry 4.0 framework, this work provides a blueprint for future ZDM solutions that prioritize sustainability, adaptability, and enhanced product quality in complex manufacturing ecosystems. Keywords: Microservices architecture, Message brokers, Industry 4.0
Integration of machine learning models in a microservices architecture
Achieving Zero Defect Manufacturing in the evolving landscape of Industry 4.0 requires advanced, scalable architectures that support proactive quality management and real-time defect detection. This thesis introduces a ZDM-focused microservices architecture designed to enhance modularity, resilience, and scalability within industrial manufacturing environments. By integrating Cyber-Physical Systems and Digital Twins, the proposed architecture facilitates continuous monitoring, dynamic data flow, and predictive analyt- ics, aligning with RAMI 4.0 standards to ensure seamless interoperability across systems. Emphasizing communication-driven design, the architecture leverages distributed microservices and specialized communication brokers to create a flexible, event-driven system. This enables efficient handling of high data volumes, real-time quality insights, and early anomaly detection. Through a structured evaluation of core architectural components, including orchestration, choreography, and communication brokers, this work establishes a foundation for ZDM implementations adaptable to various industrial settings. The architecture’s deployment in a real-world manufacturing case demonstrates its practical benefits, illustrating how modular, scalable systems can drive operational improvements and defect reduction. Contributing to the broader Industry 4.0 framework, this work provides a blueprint for future ZDM solutions that prioritize sustainability, adaptability, and enhanced product quality in complex manufacturing ecosystems. Keywords: Microservices architecture, Message brokers, Industry 4.0
Integration of machine learning models in a microservices architecture
Ibrahim, Ahmed Gamal Ali Ali (author) / Lopes, Rui Pedro
2024-01-01
203806387
Theses
Electronic Resource
English
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