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

Sheng Min Chiu, Yi Chung Chen, Cheng Ju Kuo, Li Chun Hung, Min Hsiung Hung, Chao Chun Chen, Chiang Lee

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Article number2506711
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering


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