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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.