A Multiple Signals Fusing Framework for Tool Condition Monitoring Based on Deep Learning

Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is vital to maintain the quality of tool and workpiece during machining process. Many studies for tool condition monitoring via monitoring signals based deep learning have been conducted. Each signal has a different sensitivity to a different status of tool wear. It is a key problem that how to combine the advantages of various signals and fuse the sensor signals to improve the accuracy of monitoring. This paper proposes a multiple signals fusing framework(MSFF) for tool condition monitoring based on deep learning. The monitoring signals in machining processes, including force signal, vibration signal, and acoustic emission signal, are collected and analyzed. Then, features related to tool wear on the collected signals are extracted based on deep learning and realize the mapping between the extracted features and tool condition through linear regression. The advantages and the disadvantages of different signal selection schemes based on deep learning are compared and analyzed. The experimental results show that the performance of the proposed MSFF is superior compared to other schemes for tool condition monitoring.