A Cyber-Physical System for Surface Roughness Monitoring in End-Milling

Abstract

A cyber-psychical machining system (CPMS) is developed to realize smart end-milling process monitoring. The CPMS provides a novel way for controlling the cutting chip size and monitoring the surface roughness in milling processes through Internet of Things (IoT) applications. The two level CPMS is realized by linking the IoT machining platform for process control to the machine tool with integrated visual system (VS). The VS is employed to acquire the signals of the cutting chip size during the machining of difficult to cut materials. The machining platform performs instant chip size and surface roughness control based on advanced signal processing, edge computing, modeling and cognitive corrective process control acting. A cognitive neural control system (CNCS) is employed to control the chip size by modifying the machining parameters and consequently maintaining surface roughness constant. An adaptive neural inference system (ANFIS) is applied to precisely model and in-process predict the surface roughness. Machining tests conducted using the proposed CPMS indicate that the cutting chip size and consequently the produced surface roughness are well maintained when the cutting-depth profile of a workpiece is varying step-wise or continuously.