Industry 4.0 revolutionized the concept of manufacturing systems by introducing technologies such as Industrial Internet of Things (IIoT) sensors and collaborative robots. This revolution has deeply changed manufacturing systems by increasing their interconnection and flexibility capabilities. Current manufacturing systems are no longer built as a set of standalone systems transforming input materials into finished or semi-finished products, but as a set of systems working in synergy to optimize the production. In such a context, timely response to unforeseen events (e.g., machine breakdown and production delays) is a fundamental requirement for reducing production costs and wastes. This thesis proposes a novel model-based framework titled Dynamic Manufacturing Orchestrator (DynaMO). This framework leverages System Modeling Language (SysML) as modeling language to represent the various aspects of the manufacturing system. The knowledge embedded in the models allows to create a unified layer abstracting the low-level details of the manufacturing contexts. DynaMO exploits the knowledge embedded in the abstraction layer to implement a set of methodologies to verify, optimize, and reconfigure manufacturing systems. These methodologies include from real-time anomaly detection, requirements verification, reconfiguration plan generation, and scheduling. The proposed framework has been prototyped within the ICE laboratory, a research facility containing a fully-fledged production line. This integration fully unleashes the production line’s flexibility, showcasing the advantages of the proposed framework. Moreover, a portion of the work presented in this thesis has led to the creation of the startup company Factoryal Srl, which aims to bring the proposed methodologies to the market.
L’Industria 4.0 ha trasformato profondamente il panorama dei sistemi di produzione, introducendo tecnologie avanzate come i sensori IIoT industriali e i robot collaborativi. Questa rivoluzione ha aumentato significativamente l’interconnessione e la capacità di riconfigurazione dei sistemi produttivi, che non sono più concepiti come unità isolate incaricate di trasformare materiali grezzi in prodotti finiti. Al contrario, i moderni sistemi di produzione operano in sinergia, ottimizzando processi e risorse per migliorare l’efficienza globale. In questo contesto, la capacità di rispondere tempestivamente a eventi imprevisti (e.g., guasti delle macchine e ritardi nella produzione) è un requisito fondamentale per ridurre i costi di produzione e gli sprechi. Questa tesi presenta DynaMO, un framework innovativo basato su modelli, progettato per affrontare queste sfide. DynaMO sfrutta SysML come linguaggio di modellazione per rappresentare in modo integrato i diversi aspetti di un sistema di produzione. La conoscenza contenuta in questi modelli permette di creare uno strato unificato che astrae i dettagli a basso livello dei vari contesti manifatturieri. DynaMO sfrutta la conoscenza contenuta nello strato di astrazione per implementare un insieme di metodologie per verificare, ottimizzare e riconfigurare i sistemi di produzione. Queste metodologie includono la rilevazione in tempo reale di anomalie, la verifica dei requisiti, la generazione di piani di riconfigurazione e la schedulazione della produzione. Il framework proposto è stato prototipato all’interno del laboratorio ICE, una struttura di ricerca che contiene una linea di produzione completamente attrezzata. Questa integrazione permette di sfruttare appieno la flessibilità della linea di produzione, dimostrando i vantaggi del framework proposto. Inoltre, una parte del lavoro presentato in questa tesi ha portato alla creazione della startup Factoryal Srl, che mira a portare le metodologie proposte sul mercato.
DynaMO: A Framework to Verify, Optimize and Reconfigure Flexible Manufacturing Systems
GAIARDELLI, SEBASTIANO
2025
Abstract
Industry 4.0 revolutionized the concept of manufacturing systems by introducing technologies such as Industrial Internet of Things (IIoT) sensors and collaborative robots. This revolution has deeply changed manufacturing systems by increasing their interconnection and flexibility capabilities. Current manufacturing systems are no longer built as a set of standalone systems transforming input materials into finished or semi-finished products, but as a set of systems working in synergy to optimize the production. In such a context, timely response to unforeseen events (e.g., machine breakdown and production delays) is a fundamental requirement for reducing production costs and wastes. This thesis proposes a novel model-based framework titled Dynamic Manufacturing Orchestrator (DynaMO). This framework leverages System Modeling Language (SysML) as modeling language to represent the various aspects of the manufacturing system. The knowledge embedded in the models allows to create a unified layer abstracting the low-level details of the manufacturing contexts. DynaMO exploits the knowledge embedded in the abstraction layer to implement a set of methodologies to verify, optimize, and reconfigure manufacturing systems. These methodologies include from real-time anomaly detection, requirements verification, reconfiguration plan generation, and scheduling. The proposed framework has been prototyped within the ICE laboratory, a research facility containing a fully-fledged production line. This integration fully unleashes the production line’s flexibility, showcasing the advantages of the proposed framework. Moreover, a portion of the work presented in this thesis has led to the creation of the startup company Factoryal Srl, which aims to bring the proposed methodologies to the market.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/203085
URN:NBN:IT:UNIVR-203085