Multi-stage production systems concede for low error and failure margins within every single machining and assembly step to not degrade product quality. Especially during multi-stage production of rotating parts, minor defects during a single step can corrupt a workpiece beyond repair. Since multistage production systems are complex, inter-connected chains of machining steps, a global approach to handling and compensating error emergence and propagation is for reaching Zero-Defect Manufacturing indispensable. We introduce Part Variation Modeling within a knowledge capturing platform to monitor centrally gathered metrological data for deviations. Further, a parametric model is presented allowing for description of rotating parts and enabling identification of deviations at every stage. Based on our inter-stage correlation analysis technique, the parametric model enables description of Part Variation Modes of a piece given current machine states and historic deviation likelihood as will be presented.

Part Variation Modeling in Multi-Stage Production Systems for Zero-Defect Manufacturing

Magnanini, Maria Chiara;Colledani, Marcello;
2019

Abstract

Multi-stage production systems concede for low error and failure margins within every single machining and assembly step to not degrade product quality. Especially during multi-stage production of rotating parts, minor defects during a single step can corrupt a workpiece beyond repair. Since multistage production systems are complex, inter-connected chains of machining steps, a global approach to handling and compensating error emergence and propagation is for reaching Zero-Defect Manufacturing indispensable. We introduce Part Variation Modeling within a knowledge capturing platform to monitor centrally gathered metrological data for deviations. Further, a parametric model is presented allowing for description of rotating parts and enabling identification of deviations at every stage. Based on our inter-stage correlation analysis technique, the parametric model enables description of Part Variation Modes of a piece given current machine states and historic deviation likelihood as will be presented.
Proceedings of 2019 IEEE International Conference on Industrial Technology (ICIT)
978-1-5386-6376-9
Production Systems, Intelligent Manufacturing, Data Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1093511
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