Skip to main content Accessibility help
×
×
Home

Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks

  • Dipendra Jha (a1), Saransh Singh (a2), Reda Al-Bahrani (a1), Wei-keng Liao (a1), Alok Choudhary (a1), Marc De Graef (a2) and Ankit Agrawal (a1)...
Abstract

We present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline nickel. The deep learning model results in a mean disorientation error of 0.548° compared to 0.652° using dictionary based indexing.

Copyright
Corresponding author
*Author for correspondence: Ankit Agrawal, E-mail: ankitag@eecs.northwestern.edu
References
Hide All
Abadi, M, Agarwal, A, Barham, P, Brevdo, E, Chen, Z, Citro, C, Cor-rado, GS, Davis, A, Dean, J, Devin, M, Ghemawat, S, Goodfellow, I, Harp, A, Irving, G, Isard, M, Jia, Y, Jozefowicz, R, Kaiser, L, Kudlur, M, Levenberg, J, Mane, D, Monga, R, Moore, S, Murray, D, Olah, C, Schuster, M, Shlens, J, Steiner, B, Sutskever, I, Talwar, K, Tucker, P, Vanhoucke, V, Vasudevan, V, Viegas, F, Vinyals, O, Warden, P, Wattenberg, M, Wicke, M, Yu, Y Zheng, X (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint:160304467.
Adams, BL, Wright, SI Kunze, K (1993) Orientation imaging: The emergence of a new microscopy. Metall Trans A Phys Metall Mater Sci 24, 819831.
Agrawal, A Choudhary, A (2016) Perspective: Materials informatics and big data: Realization of the fourth paradigm of science in materials science. APL Mater 4, 053208.
An, N, Zhao, W, Wang, J, Shang, D Zhao, E (2013) Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 49, 279288.
Bezijglov, A, Blanton, B Santiago, R (2016) Multi-output artificial neural network for storm surge prediction in north carolina. arXiv preprint: 160907378.
Bottou, L (1991) Stochastic gradient learning in neural networks. In Proceedings of Neuro-Nimes 91, 4th International Conference on Neural Networks and their Applications. Nanterre, France: EC2.
Briggs, F, Fern, XZ Raich, R (2013) Context-aware miml instance annotation. In 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 41–50. Piscataway, NJ: IEEE.
Callahan, PG De Graef, M (2013) Dynamical electron backscatter diffraction patterns. part I: Pattern simulations. Microsc Microanal 19, 1255–1265.
Cecen, A, Dai, H, Yabansu, YC, Kalidindi, SR Song, L (2018) Material structure–property linkages using three-dimensional convolutional neural networks. Acta Mater 146, 7684.
Chen, YH, Park, SU, Wei, D, Newstadt, G, Jackson, MA, Simmons, JP, De Graef, M Hero, AO (2015) A dictionary approach to electron backscatter diffraction indexing. Microsc Microanal 21, 739752.
Collobert, R Weston, J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, Lawrence N and Reid M (eds.), pp. 160–167. Helsinki, Finland: PMLR.
Deng, L, Li, J, Huang, JT, Yao, K, Yu, D, Seide, F, Seltzer, M, Zweig, G, He, X, Williams, J, Gong, Y Acero, A (2013) Recent advances in deep learning for speech research at Microsoft. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8604–8608. Piscataway, NJ: IEEE.
Gopalakrishnan, K, Khaitan, SK, Choudhary, A Agrawal, A (2017) Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr Build Mater 157, 322330.
He, K, Zhang, X, Ren, S Sun, J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770778. Piscataway, NJ: IEEE.
Kang, K, Oh, JH, Kwon, C Park, Y (1996) Generalization in a two-layer neural network with multiple outputs. Phys Rev E 54, 18111815.
Kingma, D Ba, J (2014) Adam: A method for stochastic optimization. arXiv preprint:14126980.
Kondo, R, Yamakawa, S, Masuoka, Y, Tajima, S Asahi, R (2017) Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics. Acta Mater 141, 2938.
Krieger Lassen, N (1992) Automatic crystal orientation determination from ebsps. Micron Microsc Acta 6, 191192.
Krizhevsky, A, Sutskever, I Hinton, GE (2012) Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems 25, pp. 10971105. San Francisco, CA: Morgan Kaufmann Publishers Inc.
LeCun, Y (2015) Lenet-5, convolutional neural networks. http://yannlecuncom/exdb/lenet (retrieved March 15, 2018).
LeCun, Y, Bengio, Y Hinton, G (2015) Deep learning. Nature 521, 436444.
Ling, J, Hutchinson, M, Antono, E Decost, B (2017) Building data-driven models with microstructural images: Generalization and interpretability. Mater Dis 10, 1928.
Liu, R, Agrawal, A, Liao, WK, Choudhary, A De Graef, M (2016) Materials discovery: Understanding polycrystals from large-scale electron patterns. In 2016 IEEE International Conference on Big Data (Big Data), December 5–8, Washington DC, pp. 2261–2269. Piscataway, NJ: IEEE.
Marquardt, K, De Graef, M, Singh, S, Marquardt, H, Rosenthal, A Koizuimi, S (2017) Quantitative electron backscatter diffraction (EBSD) data analyses using the dictionary indexing (DI) approach: Overcoming indexing difficulties on geological materials. Am Mineral 102, 18431855.
Mikolov, T, Deoras, A, Povey, D, Burget, L Černockỳ, J (2011) Strategies for training large scale neural network language models. In 2011 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), December 11–15, Waikoloa, HI, pp. 196–201. Piscataway, NJ: IEEE.
Park, WB, Chung, J, Jung, J, Sohn, K, Singh, SP, Pyo, M, Shin, N Sohn, KS (2017) Classification of crystal structure using a convolutional neural network. IUCrJ 4, 486494.
Pham, A, Raich, R, Fern, X Arriaga, JP (2015) Multi-instance multi-label learning in the presence of novel class instances. In International Conference on Machine Learning, Vol. 37, Lawrence N and Reid M (eds.), pp. 24272435. Lille, France: PMLR.
Ram, F, Wright, S, Singh, S Graef, MD (2017) Error analysis of the crystal orientations obtained by the dictionary approach to EBSD indexing. Ultramicroscopy 181, 1726.
Schütt, KT, Sauceda, HE, Kindermans, PJ, Tkatchenko, A Müller, KR (2018) Schnet – A deep learning architecture for molecules and materials. J Chem Phys 148, 241722.
Schwartz, A, Kumar, M, Adams, B Field, D (eds.) (2000) Electron Backscatter Diffraction in Materials Science, 2nd ed. New York, NY: Springer.
Singh, S De Graef, M (2016) Orientation sampling for dictionary-based diffraction pattern indexing methods. Model Simul Mater Sci Eng 24, 085024.
Singh, S De Graef, M (2017) Dictionary indexing of electron channeling patterns. Microsc Microanal 23, 110.
Sutskever, I, Vinyals, O Le, QV (2014) Sequence to sequence learning with neural networks. In Advances in neural information processing systems 27, pp. 31043112. San Francisco, CA: Morgan Kaufmann Publishers Inc.
Szegedy, C, Ioffe, S, Vanhoucke, V Alemi, AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. AAAI 4, 12.
Szegedy, C, Vanhoucke, V, Ioffe, S, Shlens, J Wojna, Z (2016) Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 28182826. Piscataway, NJ: IEEE.
Van den Oord, A, Dieleman, S Schrauwen, B (2013) Deep content-based music recommendation. In Advances in neural information processing systems 26, pp. 26432651. San Francisco, CA: Morgan Kaufmann Publishers Inc.
Wright, SI, Nowell, MM, Lindeman, SP, Camus, PP, Graef, MD Jackson, MA (2015) Introduction and comparison of new EBSD post-processing methodologies. Ultramicroscopy 159, 8194.
Wu, Z, Ramsundar, B, Feinberg, EN, Gomes, J, Geniesse, C, Pappu, AS, Leswing, K Pande, V (2018) MoleculeNet: A benchmark for molecular machine learning. Chem Sci 9, 513530.
Xu, W LeBeau, JM (2018) A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns. Ultramicroscopy 188, 5969.
Zhou, ZH, Zhang, ML, Huang, SJ Li, YF (2008) MIML: A framework for learning with ambiguous objects. CORR abs/0808.3231, 112.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Microscopy and Microanalysis
  • ISSN: 1431-9276
  • EISSN: 1435-8115
  • URL: /core/journals/microscopy-and-microanalysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed