Journal Article

Physics-Driven Deep Feature Fusion: A Lightweight CSAKansformer Architecture for Tool Wear Diagnosis in P25 Turning

Liu, Ximin, Chang, Feng, Zhang, Huanqi, Wang, Shuqiang, Liu, Wei, Zhang, Tianyue

2026

Overview

Accurate tool wear identification is essential for ensuring the continuity of intelligent machining and workpiece quality. To address the challenges of multi-source fusion inefficiency and inadequate feature extraction, this study proposes a novel identification architecture combining physics-guided multi-channel Gramian angular field (PG-MGAF) with a minimalist 14-layer CSA-Kansformer network. Multi-source signals are preprocessed via PG-MGAF to convert 1D time-series into 2D RGB images, effectively characterizing spatial coupling and interactive energy across three channels. Subsequently, the minimalist network maps these composite features to tool states, significantly reducing computational overhead. Experimental results demonstrate that the proposed model achieves an average accuracy of 93.6% with a single-step inference latency of only 5.90 ms, significantly outperforming mainstream methods such as MobileNet-V2 and ConvNeXt. This architecture provides a high-efficiency, low-latency solution for real-time tool condition monitoring under complex industrial conditions.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Accuracy / Acoustics / Datasets / deep learning / Machining / multi-source fusion / Sensors / tool wear identification / Wavelet transforms / Working conditions