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A two-stage tool wear prediction approach based on dual fusion of multi-feature and decision-making

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Abstract

With the proliferation of sensing technology and artificial intelligence, tool wear prediction under multi-sensor signals has received widespread attention. However, there is still significant potential to explore how to effectively and adaptively extract and fuse features implied in multi-signal and leverage them for decision-making. In this work, we propose a two-stage tool wear prediction framework based on dual fusion of multi-feature and decision-making. Firstly, relying on the second-order derivative of the tool wear curves, they can be roughly categorized into three stages: slight, stable, and severe wear. Next, a scale-spatial attention mechanism-based multi-scale convolutional model is designed for each wear phase, which organically combines the extraction, fusion, and utilization of spatial and temporal features, enabling local decision-making. Notably, due to the scale-spatial attention mechanism, the model automatically aggregates and highlights important information related to the current wear phase. Finally, a dynamic weight-based decision-making fusion strategy is developed to learn a comprehensive representation of tool wear across three models. This strategy adaptively determines the probability of belonging to each wear stage from unsegmented input data. We evaluate the effectiveness of our proposed method using a real milling dataset through ablation and comparison experiments, and it is found to be superior to other advanced approaches, with an average R2 of predicted results as high as 0.9968.

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  • 20 September 2023

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Funding

This research was supported by the National Natural Science Foundation of China (NSFC) Project (51975402).

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Authors and Affiliations

  1. School of Mechanical Engineering, Tianjin University, Tianjin, 300354, People’s Republic of China

    Jingchuan Dong, Yubo Gao, Depeng Su, Xiaoxin Wu, Tao Chen & Hongyu Jiang

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  1. Jingchuan Dong
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  2. Yubo Gao
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  3. Depeng Su
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  4. Xiaoxin Wu
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  5. Tao Chen
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Contributions

Jingchuan Dong and Yubo Gao: data collection, methodology, experiment, formal analysis, writing (the first version draft), review, editing, and supervision; Depeng Su: experiment and review; Xiaoxin Wu: formal analysis and review; Tao Chen and Hongyu Jiang: experiment.

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Correspondence to Yubo Gao.

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Dong, J., Gao, Y., Su, D. et al. A two-stage tool wear prediction approach based on dual fusion of multi-feature and decision-making. Int J Adv Manuf Technol 129, 89–105 (2023). https://doi.org/10.1007/s00170-023-12259-3

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