SoC-Based Early Failure Detection System Using Deep Learning for Tool Wear

SoC-Based Early Failure Detection System Using Deep Learning for Tool Wear

Blog Article

This study aims to implement a system-on-chip (SoC) detection system for tool wear monitoring and alarms for high-precision machining processes.The proposed deep learning approach is trained by the collected sensors from real performed in a three-axial computer numerical control (CNC) machine center combined with different conditions of Horse Saddle Pads spindle speed and tightening torque.The corresponding vibrational and sound signals were collected by a three-axial accelerometer and micro-electro-mechanical-system (MEMS) microphone, and then the tool flank wear was measured by a camera.In this study, the flank wear was designed for early detection according to the ISO 8688-2:1989 standard.

A deep learning model with frequency spectrum inputs of Ethernet-Over-Power the collected signals was developed for tool wear prediction.In addition, to treat the machining variation for detection, two sensor fusion approaches are presented and implemented on an SoC-board (Pocket Beagle) for landing and cost reduction.The corresponding average detection accuracies were approximately 99.7% and 87.

75% for the single and merged models, respectively.The results demonstrated the effectiveness and performance of the proposed approach.