Abstract
A hybrid offline-online optimization, monitoring and control (HOMC) system was developed for milling processes. Safe cutting regions (SCRs) are defined based on offline analysis considering process dynamic stability, tool deflection and machined part geometric accuracy and surface quality. Near-optimum cutting conditions are defined offline using the cutter-workpiece contact information along the toolpath. A deep-learning tool condition monitoring approach is developed to detect the wear state in real-time using minimal learning efforts. The HOMC monitors the machining power signals and adaptively controls the feedrate within SCR using online predictions. Validation tests proved better process productivity, part quality and extended tool life.
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