Multi-dimensional tool wear detection using Non-Subsampled Shearlet Transform and enhanced pulse coupled neural network in optical microscopy reconstruction

Abstract

The milling tool plays a pivotal role in the fabrication of components within the aerospace and various other industrial sectors. Consequently, it is essential to perform swift, accurate, and comprehensive evaluations of tool wear throughout the manufacturing and processing stages. Traditional methods for assessing tool wear often suffer from limitations due to their dependence on singular evaluation criteria and a lack of detailed wear information. To overcome these challenges, this study introduces a multi-dimensional tool wear detection system utilizing optical microscopy vision to capture fully-focused images of the tool and reconstruct the shape of the wear region, which is designed to enable efficient, high-precision, and holistic evaluation of wear parameters. Furthermore, Non-Subsampled Shearlet Transform (NSST) and an enhanced pulse coupled neural network (PCNN)are used to extract 3D depth information, which facilitate the creation of a high-precision tool depth map by mapping high-frequency subbands to different depth levels while simultaneously obtaining the fully-focused image. Additionally, an inspection criterion is established that encompasses a multi-dimensional evaluation of wear metrics, including wear value, area, and volume. Compared to standard equipment, the error of wear value was found to be less than 0.005 mm, and the error rate of area, and volume was less than 2.5 %. Experimental results demonstrate that the proposed method offers more comprehensive assessment metrics for evaluating tool wear. It can be used to offer valuable feedback of tool state for machining processes.