Thrust Force-Based Tool Wear Estimation Using Discrete Wavelet Transformation and Artificial Neural Network in CFRP Drilling

Currently, carbon fiber-reinforced plastic (CFRP) is a material with potential uses for various industries due to its excellent properties. However, the severe tool wear in its machining is an evitable problem because it deteriorates product quality and productivity at the same time. Timely replacement of the tool should be taken in advance to reduce the influences of this drawback. In sum, the accurate estimation of the tool wear plays a crucial role in CFRP machining. Therefore, in this study, a tool wear estimation in CFRP drilling is presented. The method used is based on discrete wavelet transformation (DWT) of the thrust force signal and an artificial neural network (ANN). Two valuable features related to tool wear are identified and then extracted using DWT. The tool wear is estimated by using an ANN with two features adapted from DWT. Consequently, the tool wear, especially flank wear, in CFRP drilling can be accurately estimated using the proposed method.

Automated summary

This study presents a method for estimating tool wear in CFRP drilling, using DWT and ANN techniques to accurately extract and estimate features related to tool wear, enabling effective prediction and management of tool wear.