Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy


  • D. K. Suker Department of Mechnical Engineering, Umm Al-Qura University, Saudi Arabia


Indexing of Electron Backscatter Diffraction (EBSD) is a well-established method of crystalline material characterization that provides phase and orientation information about the crystals on the material surface. A deep learning Convolutional Neural Network was trained to predict crystal orientation from the EBSD patterns based on the mean disorientation error between the predicted crystal orientation and the ground truth. The CNN is trained using EBSD images for different deformation conditions of AA5083.


AA5083, microstructure, EBSD, machine learning, deep learning


Download data is not yet available.


A. Agrawal, K. Gopalakrishnan, and A. Choudhary, "Materials Image Informatics Using Deep Learning," in Handbook on Big Data and Machine Learning in the Physical Sciences, vol. 1, London, UK: World Scientific, 2020, pp. 205–230. DOI:

Z.-L. Wang and Y. Adachi, "Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach," Materials Science and Engineering: A, vol. 744, pp. 661–670, Jan. 2019. DOI:

L. B. Salah and F. Fourati, "Systems Modeling Using Deep Elman Neural Network," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3881–3886, Apr. 2019. DOI:

A. Agrawal and A. Choudhary, "Deep materials informatics: Applications of deep learning in materials science," MRS Communications, vol. 9, no. 3, pp. 779–792, Sep. 2019. DOI:

Z. Ding, C. Zhu, and M. De Graef, "Determining crystallographic orientation via hybrid convolutional neural network," Materials Characterization, vol. 178, May 2021, Art. no. 111213. DOI:

Z. Ding, E. Pascal, and M. De Graef, "Indexing of electron back-scatter diffraction patterns using a convolutional neural network," Acta Materialia, vol. 199, pp. 370–382, Jul. 2020. DOI:

A. J. Schwartz, M. Kumar, D. P. Field, and B. L. Adams, Electron Backscatter Diffraction in Materials Science. New York, NY, USA: Springer, 2009. DOI:

S. I. Wright, M. M. Nowell, S. P. Lindeman, P. P. Camus, M. De Graef, and M. A. Jackson, "Introduction and comparison of new EBSD post-processing methodologies," Ultramicroscopy, vol. 159, pp. 81–94, Sep. 2015. DOI:

F. J. Humphreys, "Characterisation of fine-scale microstructures by electron backscatter diffraction (EBSD)," Scripta Materialia, vol. 51, no. 8, pp. 771–776, Jul. 2004. DOI:

S. I. Wright and M. M. Nowell, "EBSD Image Quality Mapping," Microscopy and Microanalysis, vol. 12, no. 1, pp. 72–84, Feb. 2006. DOI:

S. I. Wright, M. M. Nowell, R. de Kloe, P. Camus, and T. Rampton, "Electron imaging with an EBSD detector," Ultramicroscopy, vol. 148, pp. 132–145, Jan. 2015. DOI:

R. Liu, A. Agrawal, W. Liao, A. Choudhary, and M. De Graef, "Materials discovery: Understanding polycrystals from large-scale electron patterns," in IEEE International Conference on Big Data (Big Data), Washington, DC, USA, Dec. 2016, pp. 2261–2269. DOI:

D. Jha et al., "Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks," Microscopy and Microanalysis, vol. 24, no. 5, pp. 497–502, Oct. 2018. DOI:

K. Rajan, "Materials Informatics: The Materials ‘Gene’ and Big Data," Annual Review of Materials Research, vol. 45, no. 1, pp. 153–169, 2015. DOI:

M. H. El-Axir, M. M. Elkhabeery, and M. M. Okasha, "Modeling and Parameter Optimization for Surface Roughness and Residual Stress in Dry Turning Process," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2047–2055, Oct. 2017. DOI:

C. Shu, Z. Xin, and C. Xie, "EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction," in 6th China Conference on Knowledge Graph and Semantic Computing, Guangzhou, China, Nov. 2021, pp. 3–15. DOI:

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015. DOI:

B. Zahran, "Using Neural Networks to Predict the Hardness of Aluminum Alloys," Engineering, Technology & Applied Science Research, vol. 5, no. 1, pp. 757–759, Feb. 2015. DOI:

K. Kaufmann, C. Zhu, A. S. Rosengarten, and K. S. Vecchio, "Deep Neural Network Enabled Space Group Identification in EBSD," Microscopy and Microanalysis, vol. 26, no. 3, pp. 447–457, Jun. 2020. DOI:

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. London, UK: MIT Press, 2016.

Y. Bengio, "Practical Recommendations for Gradient-Based Training of Deep Architectures," in Neural Networks: Tricks of the Trade, 2nd edition., G. Montavon, G. B. Orr, and K.-R. Muller, Eds. Berlin, Heidelberg: Springer, 2012, pp. 437–478. DOI:

Y.-F. Shen, R. Pokharel, T. J. Nizolek, A. Kumar, and T. Lookman, "Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns," Acta Materialia, vol. 170, pp. 118–131, Feb. 2019. DOI:

A. Goyal and Y. Bengio, "Inductive Biases for Deep Learning of Higher-Level Cognition," arXiv:2011.15091 [cs, stat], Feb. 2021, Accessed: Feb. 28, 2022. [Online]. Available:

A. R. Durmaz et al., "A deep learning approach for complex microstructure inference," Nature Communications, vol. 12, no. 1, Aug. 2021, Art. no. 6272. DOI:

R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, "Machine learning in materials informatics: recent applications and prospects," NPJ Computational Materials, vol. 3, Sep. 2017, Art. no. 54. DOI:

"The evaluation of grain orientation data," EBSD and BKD. (accessed Mar. 01, 2022).


How to Cite

D. K. Suker, “Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 2, pp. 8393–8401, Apr. 2022.


Abstract Views: 317
PDF Downloads: 195

Metrics Information
Bookmark and Share