Multi-sensor Data Fusion and Feature Extraction for Cutting Tool Condition Monitoring: A Review

Cutting tools are the key component for removing material in machining process. Tool wear will definitely impair machining quality and efficiency and even result in more serious consequences. With the development of intelligent algorithms, it is necessary to reveal the relation between tool condition and the data collected in actual machining, and to develop a reasonable monitoring system to ensure that the tool be replaced appropriately under certain circumstances. Aiming at tool wear monitoring in milling process, this paper discusses the commonly used sensor data types. Then, data fusion technology is addressed, and the feature recognition and tool wear monitoring methods using machine learning are covered. Finally, an implementation of residual convolution neural network is presented.