Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

doi:10.1007/s40436-024-00499-9

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 576-590.doi: 10.1007/s40436-024-00499-9

• • 上一篇

Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

Yuan-Zhe Hu¹, Ru-Xue Liu¹, Jia-Peng He¹, Guo-Wei Zhou², Da-Yong Li¹

1. State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;
2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

收稿日期:2023-09-21 修回日期:2023-11-14 发布日期:2024-09-07
通讯作者: Da-Yong Li,E-mail:dyli@sjtu.edu.cn E-mail:dyli@sjtu.edu.cn
作者简介:Yuan-Zhe Hu Doctor degree candidate, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received a B.E. degree in Mechanical Engineering from Shanghai Jiao Tong University in 2021. The main research interests focus on machine learningbased multi-scale modeling methods for aluminum alloys;
Ru-Xue Liu Doctor degree candidate, School of Mechanical Engineering, Shanghai Jiao Tong University, China. He received a Master’s degree in Vehicle Engineering from Chongqing University in 2018. His main research interest focuses on the multiscale modeling of hot forming process for aluminum alloys;
Jia-Peng He Doctor degree candidate, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received a B.E. degree in Mechanical Engineering from Shanghai Jiao Tong University in 2022. The research interests focus on biomechanics and computational solid mechanics;
Guo-Wei Zhou Doctor, Associate Professor, Doctoral Supervisor, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, China. He received a Ph.D. degree in Mechanical Engineering from Shanghai Jiao Tong University in 2016. The main research interests include modeling crystal plasticity in metallic materials, mechanical multiscale modeling of material deformation behavior and datadriven material modeling;
Da-Yong Li Doctor, Professor, Doctoral Supervisor, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received a Ph.D. degree in Mechanical Engineering from Jilin University of Technology in 2000. The main research interests include elasticplastic finite element method, CAD/CAE techniques in mechanical design and forming process and plastic forming theory and simulation technology.
基金资助:
The authors acknowledge funding from the National Natural Science Foundation of China (Grant Nos. U2141215, 52105384).

Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

Yuan-Zhe Hu¹, Ru-Xue Liu¹, Jia-Peng He¹, Guo-Wei Zhou², Da-Yong Li¹

1. State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;
2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

Received:2023-09-21 Revised:2023-11-14 Published:2024-09-07
Contact: Da-Yong Li,E-mail:dyli@sjtu.edu.cn E-mail:dyli@sjtu.edu.cn
Supported by:
The authors acknowledge funding from the National Natural Science Foundation of China (Grant Nos. U2141215, 52105384).

摘要/Abstract

摘要： Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets. Accurate roping prediction and rating are essential for industrial applications. Recently, the authors introduced an artificial neural network (ANN) model to efficiently forecast roping behavior across the thickness of large regions with texture gradients. In this study, the previously proposed ANN model for roping prediction is briefly reviewed, and a few-shot learning (FSL)-based method is developed for roping grading with limited samples. To consider the directionality of the roping patterns, the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization. A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm. A new component-focused representation is also implemented for data-processing, exploiting the close correlation between roping and power distribution in the frequency domain. The ultimate FSL method achieved an optimal accuracy of 95.65% in roping classification with only five training samples per class, outperforming four typical FSL methods. This FSL approach can be applied to grade the roping morphologies predicted by the ANN model. Consequently, the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00499-9

关键词: Roping, Artificial neural network (ANN), Aluminum alloys, Few-shot classification, Surface morphology

Abstract: Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets. Accurate roping prediction and rating are essential for industrial applications. Recently, the authors introduced an artificial neural network (ANN) model to efficiently forecast roping behavior across the thickness of large regions with texture gradients. In this study, the previously proposed ANN model for roping prediction is briefly reviewed, and a few-shot learning (FSL)-based method is developed for roping grading with limited samples. To consider the directionality of the roping patterns, the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization. A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm. A new component-focused representation is also implemented for data-processing, exploiting the close correlation between roping and power distribution in the frequency domain. The ultimate FSL method achieved an optimal accuracy of 95.65% in roping classification with only five training samples per class, outperforming four typical FSL methods. This FSL approach can be applied to grade the roping morphologies predicted by the ANN model. Consequently, the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00499-9

Key words: Roping, Artificial neural network (ANN), Aluminum alloys, Few-shot classification, Surface morphology

Yuan-Zhe Hu, Ru-Xue Liu, Jia-Peng He, Guo-Wei Zhou, Da-Yong Li. Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading[J]. Advances in Manufacturing, 2024, 12(3): 576-590.

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Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

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