A dual-branch spatial-frequency fusion network for tool-wear prediction under limited training data
Year: 2026
Type: article
Abstract: Reliable tool wear prediction is critical for achieving high precision and stable productivity in modern manufacturing systems. However, in real industrial environments, available data are often limited to a single sensor stream and only a small set of labeled training samples, which significantly constrains the accuracy of data-driven methods. To address these challenges, this paper presents a dual-branch spatial-frequency fusion network (DSFFN) designed for the small-sample regime. First, the Stockwell transform converts raw sensor signals into time–frequency representations that capture both transient and stationary components. A spatially separable module at the frontend decouples temporal and spectral processing and enlarges the receptive field with large kernels, reducing overfitting in small-sample training. A channel-group embedding module structures cross-channel interactions into group-wise subspaces and employs adaptive soft-thresholding to denoise while retaining wear-related features. The spatial-frequency aware fusion module integrates a low-frequency branch for degradation trend extraction with global-dynamic modeling branch for long-range dependencies. Then a multi-feature fusion attention mechanism adaptively emphasizes their complementary information. Experiments on real-world industrial data demonstrate that DSFFN achieves state-of-the-art accuracy and robustness, with the biggest gains at small training sizes.
Authors: Jing Yang, Jian Duan, Hu Long, Tielin Shi
Institution: Huazhong University of Science and Technology
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
Award: G2200757405
OpenAlex ID: W7127080250