Development of Lightweight RBF-DRNN and Automated Framework for CNC Tool-Wear Prediction

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 11
• Main Authors: Chiu, Sheng-Min, Chen, Yi-Chung, Kuo, Cheng-Ju, Hung, Li-Chun, Hung, Min-Hsiung, Chen, Chao-Chun, Lee, Chiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Computational modeling; Computer architecture; Computing costs; Data mining; Data models; Deep-learning model (DLM); framework; Frequency-domain analysis; Lightweight; lightweight model; Neurons; Numerical controls; Numerical prediction; radial basis function (RBF); Tool wear; tool-wear prediction (TWPred); Vibrations
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3164063

Computer numerical control (CNC) tool-wear prediction (TWPred) is an important issue in the industry. Recently, researchers have demonstrated that deep-learning models (DLMs) are effective in TWPred. However, DLMs are ill-suited to small- and medium-scale manufacturers due to high computational costs. Methods exist to reduce the computational costs of DLMs, but most of them depend on overly-complex pruning processes that are not appropriate for the low-end computers used by the above manufacturers. Therefore, we developed a lightweight DLM and an automated framework for TWPred. The framework is based on two concepts: 1) the DLM was pruned by reducing the number of input data fields so the model itself remains unchanged and 2) we designed a framework that enables the automatic establishment of a lightweight DLM. These two concepts make the overall framework applicable to small- and medium-scale manufacturers. Finally, we used real-world dataset PHM 2010 to verify that the lightweight DLM can achieve almost the same reminding useful life (RUL) accuracy as the DLM (DLM: 95.55% and lightweight DLM: 95.51%) using only 0.88% of DLM parameters, which verifies the low cost and high precision of the proposed model.