Skip to main content
SpringerLink
Log in

Detecting Defects in Materials Using Deep Convolutional Neural Networks

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12131))

Included in the following conference series:

Abstract

This paper proposes representing and detecting manufacturing defects at the micrometre scale using deep convolutional neural networks. The information theoretic notion of entropy is used to quantify the information gain or mutual information of filters throughout the network, where the deepest network layers are generally shown to exhibit the highest mutual information between filter responses and defects, and thus serve as the most discriminative features. Quantitative detection experiments based on the AlexNet architecture investigate a variety of design parameters pertaining to data preprocessing and network architecture, where the optimal architectures achieve an average accuracy of 98.54%. CNNs are relatively easy to perform and give impressive achievements in classification tasks. However, the informational complexity coming from the depth of networks represents a limit to improve their capabilities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It should be noted that the term texture in the context of materials science, almost always refers to crystallographic texture, i.e., the dominant crystallographic orientation of a set of grains, and in some cases to the morphological texture. Consequently, this confusion may not help the diffusion of texture analysis in the materials science and engineering community.

References

  1. Gauthier, G., Coster, M., Chermant, L., Chermant, J.-L.: Morphological segmentation of cutting tools. Microsc. Microanal. Microstruct. 7(5–6), 339–344 (1996)

    Article  Google Scholar 

  2. Wejrzanowski, T., Spychalski, W., Różniatowski, K., Kurzydłowski, K.: Image based analysis of complex microstructures of engineering materials. Int. J. Appl. Math. Comput. Sci. 18(1), 33–39 (2008)

    Article  Google Scholar 

  3. Lee, S.G., Mao, Y., Gokhale, A.M., Harris, J., Horstemeyer, M.F.: Application of digital image processing for automatic detection and characterization of cracked constituent particles/inclusions in wrought aluminum alloys. Mater. Charact. 60(9), 964–970 (2009)

    Article  Google Scholar 

  4. Banerjee, S., Ghosh, S.K., Datta, S., Saha, S.K.: Segmentation of dual phase steel micrograph: an automated approach. Measurement 46(8), 2435–2440 (2013)

    Google Scholar 

  5. Vanderesse, N., Anderson, M., Bridier, F., Bocher, P.: Inter- and intragranular delta phase quantitative characterization in inconel 718 by means of image analysis. J. Microsc. 261(1), 79–87 (2016)

    Article  Google Scholar 

  6. Meimandi, S., Vanderesse, N., Thibault, D., Bocher, P., Viens, M.: Macro-defects characterization in cast CA-6NM martensitic stainless steel. Mater. Charact. 124, 31–39 (2017)

    Article  Google Scholar 

  7. Dutta, T., Das, D., Banerjee, S., Saha, S.K., Datta, S.: An automated morphological classification of ferrite-martensite dual-phase microstructures. Measurement 137, 595–603 (2019)

    Google Scholar 

  8. Cord, A., Bach, F., Jeulin, D.: Texture classification by statistical learning from morphological image processing: application to metallic surfaces. J. Microsc. 239, 159–166 (2010)

    Google Scholar 

  9. Ducato, A., Fratini, L., La Cascia, M., Mazzola, G.: An automated visual inspection system for the classification of the phases of Ti-6Al-4V titanium alloy. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 362–369. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40246-3_45

    Chapter  Google Scholar 

  10. DeCost, B.L., Holm, E.A.: A computer vision approach for automated analysis and classification of microstructural image data. Comput. Mater. Sci. 110, 126–133 (2015)

    Article  Google Scholar 

  11. Jeulin, D.: Morphological probabilistic hierarchies for texture segmentation. Math. Morphol. - Theory Appl. 1(1) (2016)

    Google Scholar 

  12. Gupta, S., Sarkar, J., Kundu, M., Bandyopadhyay, N.R., Ganguly, S.: Automatic recognition of SEM microstructure and phases of steel using LBP and random decision forest operator. Measurement 151, 107224 (2020)

    Google Scholar 

  13. Chowdhury, A., Kautz, E., Yener, B., Lewis, D.: Image driven machine learning methods for microstructure recognition. Comput. Mater. Sci. 123, 176–187 (2016)

    Article  Google Scholar 

  14. DeCost, B.L., Francis, T., Holm, E.A.: Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures. Acta Materialia 133, 30–40 (2017)

    Article  Google Scholar 

  15. Lin, J., Ma, L., Yao, Y.: Segmentation of casting defect regions for the extraction of microstructural properties. Eng. Appl. Artif. Intell. 85, 150–163 (2019)

    Article  Google Scholar 

  16. Stan, T., Thompson, Z.T., Voorhees, P.W.: Optimizing convolutional neural networks to perform semantic segmentation on large materials imaging datasets: X-ray tomography and serial sectioning. Mater. Charact. 160, 110119 (2020)

    Article  Google Scholar 

  17. Dimiduk, D.M., Holm, E.A., Niezgoda, S.R.: Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integr. Mater. Manuf. Innov. 7(3), 157–172 (2018)

    Article  Google Scholar 

  18. Vander Voort, G.F.: ASM Handbook, Volume 9: Metallography And Microstructures (ASM Handbook). ASM International (2004)

    Google Scholar 

  19. Lütjering, G., Williams, J.C.: Titanium. Springer, New York (2007). https://doi.org/10.1007/978-3-540-73036-1

  20. Chrapoński, J., Szkliniarz, W.: Quantitative metallography of two-phase titanium alloys. Mater. Charact. 46(2–3), 149–154 (2001)

    Article  Google Scholar 

  21. Tiley, J., et al.: Quantification of microstructural features in \(\alpha \beta \) titanium alloys. Mater. Sci. Eng.: A 372(1–2), 191–198 (2004)

    Google Scholar 

  22. Vanderesse, N., Maire, E., Darrieulat, M., Montheillet, F., Moreaud, M., Jeulin, D.: Three-dimensional microtomographic study of widmanstätten microstructures in an alpha/beta titanium alloy. Scripta Materialia 58(6), 512–515 (2008)

    Article  Google Scholar 

  23. Li, H., Ji, Z., Yang, H.: Quantitative characterization of lamellar and equiaxed alpha phases of (\(\alpha + \beta \)) titanium alloy using a robust approach for touching features splitting. Mater. Charact. 76, 6–20 (2013)

    Article  Google Scholar 

  24. Zhao, H., Ho, A., Davis, A., Antonysamy, A., Prangnell, P.: Automated image mapping and quantification of microstructure heterogeneity in additive manufactured TI6AL4V. Mater. Charact. 147, 131–145 (2019)

    Article  Google Scholar 

  25. Sharma, H., van Bohemen, S.M.C., Petrov, R.H., Sietsma, J.: Three-dimensional analysis of microstructures in titanium. Acta Materialia 58(7), 2399–2407 (2010)

    Google Scholar 

  26. Foltz, J.W., Welk, B., Collins, P.C., Fraser, H.L., Williams, J.C.: Formation of grain boundary \(\alpha \) in \(\beta \) Ti alloys: its role in deformation and fracture behavior of these alloys. Metall. Mater. Trans. A: Phys. Metall. Mater. Sci. 42, 645–650 (2011)

    Article  Google Scholar 

  27. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets (2014)

    Google Scholar 

  28. Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., Pietikäinen, M.: From BoW to CNN: two decades of texture representation for texture classification. Int. J. Comput. Vis. 127(1), 74–109 (2018)

    Article  Google Scholar 

  29. Liu, L., et al.: Deep learning for generic object detection: A survey. CoRR, abs/1809.02165 (2018)

    Google Scholar 

  30. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates Inc. (2012)

    Google Scholar 

  31. Wang, S.H., Lv, Y.D., Sui, Y., Liu, S., Wang, S.J., Zhang, Y.D.: Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J. Med. Syst. 42(1), 2 (2018)

    Google Scholar 

  32. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (2012)

    Google Scholar 

  33. Tishby, N., Zaslavsky, N.: Deep learning and the information bottleneck principle. In: 2015 IEEE Information Theory Workshop (ITW), pp. 1–5. IEEE (2015)

    Google Scholar 

  34. Chaddad, A., Naisiri, B., Pedersoli, M., Granger, E., Desrosiers, C., Toews, M.: Modeling Information Flow Through Deep Neural Networks. arXiv e-prints, page arXiv:1712.00003, November 2017

  35. Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675 (2012)

    Google Scholar 

  36. Schindelin, J., et al.: Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676–682 (2012)

    Google Scholar 

  37. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org

    Google Scholar 

  38. Chollet, F., et al.: Keras (2015). https://keras.io

  39. Wei, J.-M., Yuan, X.-J., Qing-Hua, H., Wang, S.-Q.: A novel measure for evaluating classifiers. Expert Syst. Appl. 37(5), 3799–3809 (2010)

    Article  Google Scholar 

  40. Delgado, R., Núñez-González, J.D.: Enhancing confusion entropy as measure for evaluating classifiers. In: Graña, M., et al. (eds.) SOCO’18-CISIS’18-ICEUTE’18. AISC, vol. 771, pp. 79–89. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94120-2_8

    Chapter  Google Scholar 

  41. Andrearczyk, V., Whelan, P.F.: Using filter banks in convolutional neural networks for texture classification. Pattern Recogn. Lett. 84, 63–69 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. École de Technologie Supérieure, 1100, rue Notre-Dame Ouest, Montreal, QC, H3C 1K3, Canada

    Quentin Boyadjian, Nicolas Vanderesse, Matthew Toews & Philippe Bocher

Authors
  1. Quentin Boyadjian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas Vanderesse
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew Toews
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philippe Bocher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quentin Boyadjian .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Boyadjian, Q., Vanderesse, N., Toews, M., Bocher, P. (2020). Detecting Defects in Materials Using Deep Convolutional Neural Networks. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50347-5_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50346-8

  • Online ISBN: 978-3-030-50347-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation