Computer-Vision-Assisted Estimation of Dressing Tool Effective Width in Grinding Using Acoustic Emission and Artificial Neural Networks

Abstract

The identification and online monitoring of wear in single-point dressers is important to achieve the desired surface condition on the grinding wheel and to ensure a satisfactory outcome in the grinding process. However, tool wear is a complex phenomenon that occurs in various forms during the cutting operation and lacks an analytical model capable of representing its wear state. This study aims to develop a method for predicting the effective width of a single-point dresser based on acoustic emission and computer vision data using artificial neural networks. This approach shows satisfactory results in wear prediction, showing error percentages as low as 0.46%, 0.19%, and 0.07%.