Improved Interpretable-based Physically Guided Spatial Attention for Cross-process Parameters End Milling Cutter Wear Identification

doi:10.3901/JME.2024.12.147

Abstract

Abstract: The insufficient interpretability of deep learning has become a critical issue restraining its industrial applications. Intelligent assessment methods for tool wear state exhibit high levels of speed, automation, and intelligence; however, the end-to-end patterns and extracted features are challenging to be understood. Especially when dealing with cross-process parameters, their interpretability is poor, and their reliability is insufficient. Cross-process parameters end milling cutter wear state identification model is proposed to address this based on an improved interpretable-based physically guided spatial attention mechanism. Firstly, a physically guided spatial attention module is constructed based on the periodic discontinuity characteristics of the signals, enabling the adaptive capture of key signal fragments under cross-process parameters such as feed rate and cutting depth. Secondly, constraints are applied to the features using maximum mean discrepancy and variance, reducing the distribution discrepancies of wear features under different process parameters while enhancing the aggregation of similar wear features. Through practical cutting experiments on the tool, the effectiveness of the proposed method in improving the identification accuracy under cross-process parameters is validated. Visual analysis of the spatial attention weight distribution indicates that the proposed method's extracted features possess obvious physical interpretability. The proposed method can provide a reference for the interpretability optimization of the deep learning-based identification model of tool wear monitoring.

Key words: interpretability deep learning, tool wear, spatial attention, physical characteristics

CLC Number:

TP206

LAI Xuwei, DING Kun, ZHANG Kai, HUANG Fengfei, ZHENG Qing, LI Zhixuan, DING Guofu. Improved Interpretable-based Physically Guided Spatial Attention for Cross-process Parameters End Milling Cutter Wear Identification[J]. Journal of Mechanical Engineering, 2024, 60(12): 147-157.

References

[1] TRÖBER P，WEISS H A，KOPP T，et al. On the correlation between thermoelectricity and adhesive tool wear during blanking of aluminum sheets[J]. International Journal of Machine Tools and Manufacture，2017，118/119：91-97.
[2] 狄子钧，袁东风，李东阳，等. 基于多尺度-高效通道注意力网络的刀具故障诊断方法[J]. 机械工程学报，2024，60(6)：82-90. DI Zijun，YUAN Dongfeng，LI Dongfang，et al. Tool fault diagnosis method based on multiscale-efficient channel attention network[J]. Journal of Mechanical Engineering，2024，60(6)：82-90.
[3] DOU J，XU C，JIAO S，et al. An unsupervised online monitoring method for tool wear using a sparse auto-encoder[J]. International Journal of Advanced Manufacturing Technology，2020，106：9-12.
[4] CHEN Y，JIN Y，JIRI G. Predicting tool wear with multi-sensor data using deep belief networks[J]. International Journal of Advanced Manufacturing Technology，2018，99：1917-1926.
[5] ZOU Y，DING K，SHI K，et al. Wear identification of end mills based on a feature-weighted convolutional neural network under unbalanced samples[J]. Journal of Manufacturing Processes，2023，89：64-76.
[6] ZHOU Y，ZHI G，CHEN W，et al. A new tool wear condition monitoring method based on deep learning under small samples[J]. Measurement，2022，189：110622.
[7] 史珂铭，邹益胜，刘永志，等.一种不同工艺条件下刀具磨损状态多类域适应迁移辨识方法[J]. 中国机械工程，2022，33(15)：1841-1849. SHI Keming，ZOU Yisheng，LIU Yongzhi，et al. A multi class domain adaptive transfer identification method for tool wear states under different processing conditions[J]. China Mechanical Engineering，2022，33(15)：1841-1849.
[8] HE J，SUN Y，YIN C，et al. Cross-domain adaptation network based on attention mechanism for tool wear prediction[J/OL]. Journal of Intelligent Manufacturing，2022：1-23. https://link.springer.com/article/10.1007/ s10845-022-02005-z#citeas.
[9] GUO H，ZHANG Y，ZHU K. Interpretable deep learning approach for tool wear monitoring in high-speed milling[J]. Computers in Industry，2022，138：103638.
[10] WANG F，ZHAO Z，ZHAI Z，et al. Explainability-driven model improvement for SOH estimation of lithium-ion battery[J]. Reliability Engineering and System Safety，2023，232：109046.
[11] MILLER T. Explanation in artificial intelligence：Insights from the social sciences[J]. Artificial Intelligence：An International Journal，2019，267：1-38.
[12] SELVARAJU R R，COGSWELL M，DAS A，et al. Grad-CAM：Visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision，2020，128(2)：336-359.
[13] BACH S，BINDER A，MONTAVON G，et al. On pixel-wise explanations for nonlinear classifier decisions by layer-wise relevance propagation[J]. PLoS ONE，2015，10(7)：1-46.
[14] CHEN Q，DONG X，TU G，et al. TFN：An interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis[J]. arXiv:2209.01992，2022.
[15] WANG D，CHEN Y，SHEN C，et al. Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring[J]. Mechanical Systems and Signal Processing，2022，168：108673.
[16] LI T，ZHAO Z，SUN C，et al. WaveletKernelNet：An interpretable deep neural network for industrial intelligent diagnosis[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2022，54(4)：2302-2312.
[17] ZHAO Z，LI T，AN B，et al. Model-driven deep unrolling：Towards interpretable deep learning against noise attacks for intelligent fault diagnosis[J]. ISA Transactions，2022，129(B)：644-662.
[18] WANG J，LI Y，ZHAO R，et al. Physics guided neural network for machining tool wear prediction[J]. Journal of Manufacturing Systems，2020，57：298-310.
[19] LI Y，WANG J，HUANG Z，et al. Physics-informed meta learning for machining tool wear prediction[J]. Journal of Manufacturing Systems，2022，62：17-27.
[20] ZHANG W，LI C，PENG G，et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing，2018，100：439-453.
[21] GRETTON A，BORGWARDT K M，RASCH M，et al. A kernel method for the two-sample-problem[C/CD]// Advances in Neural Information Processing Systems. 2007.
[22] ZOU Y，LIU Y，DENG J，et al. A novel transfer learning method for bearing fault diagnosis under different working conditions[J]. Measurement，2021，171：108767.
[23] MOHAMED A，HASSAN M，SAOUBI R M，et al. Tool condition monitoring for high-performance machining systems-a review[J]. Sensors，2022，22(6)：2206.
[24] ZHANG Y，ZHU K，DUAN X，et al. Tool wear estimation and life prognostics in milling：Model extension and generalization[J]. Mechanical Systems and Signal Processing，2021，155：107617.
[25] GANIN Y，USTINOVA E，AJAKAN H，et al. Domain-adversarial training of neural networks[J]. Advances in Computer Vision and Pattern Recognition，2017，17：189-209.
[26] SUN B，SAENKO K. Deep CORAL：Correlation alignment for deep domain adaptation[J]. Lecture Notes in Computer Science，2016，9915：443-450.
[27] van der MAATEN L. Accelerating t-SNE using tree-based algorithms[J]. Journal of Machine Learning Research，2015，15：3221-3245.