The geometric tolerance of notching machines used in the fabrication of components for\ninduction motor stators and rotators is less than 50 microm. The blunt edges of worn molds can cause\nthe edge of the sheet metal to form a burr, which can seriously impede assembly and reduce the\nefficiency of the resulting motor. The overuse of molds without sufficient maintenance leads to\nwasted sheet material, whereas excessive maintenance shortens the life of the punch/die plate.\nDiagnosing the mechanical performance of die molds requires extensive experience and finegrained\nsensor data. In this study, we embedded polyvinylidene fluoride (PVDF) films within the\nmechanical mold of a notching machine to obtain direct measurements of the reaction forces\nimposed by the punch. We also developed an automated diagnosis program based on a support\nvector machine (SVM) to characterize the performance of the mechanical mold. The proposed cyberphysical\nsystem (CPS) facilitated the real-time monitoring of machinery for preventative\nmaintenance as well as the implementation of early warning alarms. The cloud server used to gather\nmold-related data also generated data logs for managers. The hyperplane of the CPS-PVDF was\ncalibrated using a variety of parameters pertaining to the edge characteristics of punches. Stereomicroscopy\nanalysis of the punched workpiece verified that the accuracy of the fault classification\nwas 97.6%.
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