An Improved Deep Learning Model for Online Tool Condition Monitoring Using Output Power Signals

• Main Authors: Lang Dai, Tianyu Liu, Zhongyong Liu, Lisa Jackson, Paul Goodall, Changqing Shen, Lei Mao
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Shock and Vibration
• Online Access: http://dx.doi.org/10.1155/2020/8843314

Something like normal functionality of tools in a manufacturing process is typically designed to ensure reliability, where fast and accurate identification of tool abnormal operation plays a vital role in intelligent manufacturing. In this study, a novel method is proposed to assess the cutting tool condition, which consists of a convolutional neural network with wider first-layer kernels (W-CONV), and long short-term memory (LSTM). The analysis benefits from the use of output power signals from the cutting tool, since they can be obtained easily and efficiently, enabling the proposed method to be applicable in practical operation for online condition monitoring. Moreover, effectiveness of the proposed method is investigated, using test data from cutting tools at various tool wear conditions. Results demonstrate that with the proposed method, tool wear condition can be identified accurately and efficiently. Furthermore, with test data collected at cutting tools with different sizes, the robustness of the proposed method can be further clarified.