On-line milling tool wear monitoring under practical machining conditions

Abstract

Accurate diagnosis of milling tool wear monitoring during high-speed cutting processes is essential for ensuring machining surface precision, enhancing tool efficiency, and extending the service life of machine tools. However, challenges in data screening during each stage of tool movement and signal migration caused by changes in machining parameters have not been sufficiently addressed. To tackle these issues, this paper presents a novel MB-DAAN model, which considers signals from each stage of tool movement and diagnoses tool wear under dynamic machining parameters. Firstly, the multi-branch classification module is introduced, incorporating two independent classifiers to form a multi-branch classification module. This module adaptively extracts features of tool wear states during the cutting stage. Subsequently, the dynamic adversarial factor adjusts the global and local losses between features, aligning conditional and edge distributions across different machining parameters. The models were evaluated in the milling tool wear data set. MB-DAAN achieving a diagnostic accuracy of 97.3%-a significant improvement compared to CNN, DAN, and DAAN models.