Title: Tool Breakage Detection using Deep Learning,
Abstract: In manufacture, steel and other metals are mainly cut and shaped during the
fabrication process by computer numerical control (CNC) machines. To keep high
productivity and efficiency of the fabrication process, engineers need to
monitor the real-time process of CNC machines, and the lifetime management of
machine tools. In a real manufacturing process, breakage of machine tools
usually happens without any indication, this problem seriously affects the
fabrication process for many years. Previous studies suggested many different
approaches for monitoring and detecting the breakage of machine tools. However,
there still exists a big gap between academic experiments and the complex real
fabrication processes such as the high demands of real-time detections, the
difficulty in data acquisition and transmission. In this work, we use the
spindle current approach to detect the breakage of machine tools, which has the
high performance of real-time monitoring, low cost, and easy to install. We
analyze the features of the current of a milling machine spindle through tools
wearing processes, and then we predict the status of tool breakage by a
convolutional neural network(CNN). In addition, we use a BP neural network to
understand the reliability of the CNN. The results show that our CNN approach
can detect tool breakage with an accuracy of 93%, while the best performance of
BP is 80%.