Publication
Integration of machine learning models in a microservices architecture
| datacite.subject.fos | Engenharia e Tecnologia::Outras Engenharias e Tecnologias | pt_PT |
| dc.contributor.advisor | Lopes, Rui Pedro | |
| dc.contributor.author | Ibrahim, Ahmed Gamal Ali Ali | |
| dc.date.accessioned | 2025-01-17T14:31:54Z | |
| dc.date.available | 2025-01-17T14:31:54Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | 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 | pt_PT |
| dc.identifier.tid | 203806387 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10198/31017 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | pt_PT |
| dc.subject | Microservices architecture | pt_PT |
| dc.subject | Message broker | pt_PT |
| dc.subject | Industry 4.0 | pt_PT |
| dc.title | Integration of machine learning models in a microservices architecture | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Informática | pt_PT |