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

doi:10.1007/s40436-024-00499-9

Abstract

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.

TrendMD

References

1. Bloeck M (2012) Aluminium sheet for automotive applications. In:Rowe J (ed) Advanced materials in automotive engineering. Elsevier, Amsterdam, pp 85-108
2. Shi Y, Jin H, Wu PD et al (2017) Analysis of roping in an AA6111 T4P automotive sheet in 3D deformation states. Acta Mater 124:598-607
3. Engler O, Schäfer C, Brinkman HJ (2012) Crystal-plasticity simulation of the correlation of microtexture and roping in AA 6xxx Al-Mg-Si sheet alloys for automotive applications. Acta Mater 60:5217-5232
4. Guillotin A, Guiglionda G, Maurice C et al (2010) Quantification of roping intensity on aluminium sheets by areal power spectral density analysis. Mater Charact 61:1119-1125
5. Guillotin A, Guiglionda G, Maurice C et al (2011) Correlation of surface roping with through-thickness microtextures in an AA6xxx sheet. Metall Mater Trans A 42:1919-1924
6. Wu PD, Lloyd DJ, MacEwen SR (2003) A simple model describing roping in A1 sheet. Scr Mater 48:1243-1248
7. Qin L, Seefeldt M, Van Houtte P (2015) Analysis of roping of aluminum sheet materials based on the meso-scale moving window approach. Acta Mater 84:215-228
8. Hu Y, Zhou G, Yuan X et al (2023) An artificial neural network-based model for roping prediction in aluminum alloy sheet. Acta Mater 245:118605. https://doi.org/10.1016/j.actamat.2022.118605
9. Marteau J, Deltombe R, Bigerelle M (2020) Quantification of the morphological signature of roping based on multiscale analysis and autocorrelation function description. Materials 13:3040. https://doi.org/10.3390/ma13133040
10. Schäfer C, Brinkman HJ, Engler O et al (2015) Quantification of roping in aluminium sheet alloys for car body applications by combining 3D surface measurements with Fourier analysis. Int J Mater Res 106:248-257
11. Hu Y, Zhou G, Liu R et al (2023) On the correlation between roping, texture, and morphology of aluminium alloy sheets. J Mater Res Technol 26:571-586
12. Wei B, Hao K, Tang X et al (2019) A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes. Text Res J 89:3539-3555
13. Mundt M, Majumder S, Murali S et al (2019) Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrete defect bridge image dataset. In:Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11196-11205
14. Konovalenko I, Maruschak P, Brezinová J et al (2020) Steel surface defect classification using deep residual neural network. Metals 10:846. https://doi.org/10.3390/met10060846
15. Mangla P, Kumari N, Singh M et al (2020) Charting the right manifold:Manifold mixup for few-shot learning. In:Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 2218-2227
16. Hu Y, Vincent G, Stéphane P (2021) Leveraging the feature distribution in transfer-based few-shot learning. In:International conference on artificial neural networks. Springer International Publishing, Cham, pp 487-499
17. Chen WY, Liu YC, Kira Z et al (2018) A closer look at few-shot classification. In:International conference on learning representations. arXiv:1904.04232. https://doi.org/10.48550/arXiv.1904.04232
18. Lichtenstein M, Sattigeri P, Feris R et al (2020) TAFSSL:task-adaptive feature sub-space learning for few-shot classification. In European conference on computer vision, Springer, Cham, pp 522-539
19. Zhang H, Cisse M, Dauphin YN et al (2018) Mixup:beyond empirical risk minimization. In:international conference on learning representations, arXiv:1710.09412. https://doi.org/10.48550/arXiv.1710.09412
20. Verma V, Lamb A, Beckham C, et al (2019) Manifold mixup:better representations by interpolating hidden states. In:International conference on machine learning, PMLR, pp 6438-6447
21. GMW-15420(2012) Global qualification process for aluminum sheet. General Motors, US
22. Vinyals O, Blundell C, Lillicrap T et al (2016) Matching networks for one shot learning. Adv Neural Inf Process. arXiv:1606.04080. https://arxiv.org/abs/1606.04080
23. Russakovsky O, Deng J, Su H et al (2015) ImageNet large scale visual recognition challenge. IJCV 115:211-252
24. Zagoruyko S, Komodakis N (2016) Wide residual networks. In:British machine vision conference. arXiv:1605.07146. https://doi.org/10.48550/arXiv.1605.07146
25. Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In:International conference on machine learning, PMLR, pp 1126-1135
26. Munkhdalai T, Yu H (2017) Meta networks. In:International conference on machine learning, PMLR, pp 2554-2563
27. Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process. arXiv:1703.05175. https://arxiv.org/abs/1703.05175
28. Zhou B, Khosla A, Lapedriza A, et al (2016) Learning deep features for discriminative localization. In:Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR, pp 2921-2929
29. Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579-2605
30. McInnes L, Healy J, Saul N et al (2018) UMAP:uniform manifold approximation and projection. arXiv:1802.03426. https://doi.org/10.48550/arXiv.1802.03426

[1]	Qiang-Qiang Zhai, Zhao Liu, Ping Zhu. Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation [J]. Advances in Manufacturing, 2024, 12(3): 497-511.
[2]	Zhao-Qing Zhang, Kai-Ning Shi, Yao-Yao Shi, Yi-Hui Song, Zhe He, Ya-Song Pu. Study on a probabilistic algorithm for the forming and 3D characterization of special-shaped surfaces under profile grinding [J]. Advances in Manufacturing, 2024, 12(2): 288-299.
[3]	Omar Ahmed Mohamed, Syed Hasan Masood, Jahar Lal Bhowmik. Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using defi nitive screening design and deep learning feedforward artifi cial neural network [J]. Advances in Manufacturing, 2021, 9(1): 115-129.
[4]	Abhilash P. M., Chakradhar D.. Prediction and analysis of process failures by ANN classification during wire-EDM of Inconel 718 [J]. Advances in Manufacturing, 2020, 8(4): 519-536.
[5]	Saroj Kumar Padhi, Ranjeet Kumar Sahu, S. S. Mahapatra, Harish Chandra Das, Anoop Kumar Sood, Brundaban Patro, A. K. Mondal. Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy [J]. Advances in Manufacturing, 2017, 5(3): 231-242.
[6]	Mustafa Bakkal, Erdinç Serbest, İlker Karipçin, Ali T. Kuzu, Umut Karagüzel, Bora Derin. An experimental study on grinding of Zr-based bulk metallic glass [J]. Advances in Manufacturing, 2015, 3(4): 282-291.
[7]	Ke-Sheng Wang, Zhe Li, Jørgen Braaten, Quan Yu. Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs [J]. Advances in Manufacturing, 2015, 3(2): 97-104.
[8]	Zhen-You Zhang Ke-Sheng Wang. Wind turbine fault detection based on SCADA data analysis using ANN [J]. Advances in Manufacturing, 2014, 2(1): 70-78.