Skip to main content
SpringerLink
Log in

Automated Defect Recognition of Castings Defects Using Neural Networks

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

Industrial X-ray analysis is common in aerospace, automotive or nuclear industries where structural integrity of some parts needs to be guaranteed. However, the interpretation of radiographic images is sometimes difficult and may lead to two experts disagree on defect classification. The automated defect recognition (ADR) system presented herein will reduce the analysis time and will also help reducing the subjective interpretation of the defects while increasing the reliability of the human inspector. Our convolutional neural network (CNN) model achieves 0.942 mAP@IoU = 0.50, which is considered as similar to expected human performance, when applied to an automotive aluminium castings dataset (GDXray). On an industrial environment, its inference time is less than 400 ms per 16 GB DICOM image (16 bits), so it can be installed on production facilities with no impact on delivery time. In addition, an ablation study of the main hyper-parameters to optimise model accuracy from the initial baseline result of 0.75 mAP up to 0.942 mAP, was also conducted.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. See https://cocodataset.org/#detection-eval

  2. See https://cocodataset.org/#home

References

  1. Seeram, E.: Continuous quality improvement for digital radiography. In: Seeram, E. (ed.) Digital Radiography: Physical Principles and Quality Control, pp. 185–211. Springer, Singapore (2019)

    Chapter  Google Scholar 

  2. Avalle, M., Belingardi, G., Cavatorta, M.P., Doglione, R.: Casting defects and fatigue strength of a die cast aluminium alloy: a comparison between standard specimens and production components. Int. J. Fatigue 24(1), 1–9 (2002). https://doi.org/10.1016/S0142-1123(01)00112-8

    Article  Google Scholar 

  3. Matzkanin, G.A., Yolken, H.T.: Probability of Detection (POD) for Nondestructive Evaluation (NDE):. Tech. rep., Defense Technical Information Center, Fort Belvoir, VA (2001). https://doi.org/10.21236/ADA398282

  4. Zhao, Z.Q., Zheng, P., Xu, S.t., Wu, X.: Object detection with deep learning: a review. arXiv:1807.05511 [cs] (2019)

  5. Mery, D.: Aluminum casting inspection using deep object detection methods and simulated ellipsoidal Defects. Mach. Vis. Appl. (2021). https://doi.org/10.1007/s00138-021-01195-5

    Article  Google Scholar 

  6. Yang, L., Wang, H., Huo, B., Li, F., Liu, Y.: An automatic welding defect location algorithm based on deep learning. NDT & E Int. 120, 102435 (2021). https://doi.org/10.1016/j.ndteint.2021.102435

    Article  Google Scholar 

  7. Gamdha, D., Unnikrishnakurup, S., Rose, K.J.J., Surekha, M., Purushothaman, P., Ghose, B., Balasubramaniam, K.: Automated defect recognition on X-ray radiographs of solid propellant using deep learning based on convolutional neural networks. J. Nondestr. Eval. 40(1), 18 (2021). https://doi.org/10.1007/s10921-021-00750-4

    Article  Google Scholar 

  8. Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: Unsupervised dual learning for image-to-image translation. arXiv:1704.02510 [cs] (2018)

  9. Ajmi, C., Zapata, J., Martínez-Álvarez, J.J., Doménech, G., Ruiz, R.: Using deep learning for defect classification on a small weld X-ray image dataset. J. Nondestr. Eval. 39(3), 68 (2020). https://doi.org/10.1007/s10921-020-00719-9

    Article  Google Scholar 

  10. Ferguson, M., Ak, R., Lee, Y.T.T., Law, K.H.: Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. arXiv:1808.02518 [cs] (2018)

  11. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. arXiv:1708.02002 [cs] (2018)

  12. Mery, D., Riffo, V., Zscherpel, U., Mondragon, G., Lillo, I., Zuccar, I., Lobel, H., Carrasco, M.: GDXray: the database of X-ray images for nondestructive testing. J. Nondestr. Eval. 34(4), 42 (2015). https://doi.org/10.1007/s10921-015-0315-7

    Article  Google Scholar 

  13. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. arXiv:1512.02325 [cs] 9905, 21–37 (2016). https://doi.org/10.1007/978-3-319-46448-0-2

  14. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497 [cs] (2016)

  15. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. arXiv:1703.06870 [cs] (2018)

  16. Boerner, H., Strecker, H.: Automated X-ray inspection of aluminum castings. IEEE Trans. Pattern Anal. Mach. Intell. 10(1), 79–91 (1988). https://doi.org/10.1109/34.3869

    Article  Google Scholar 

  17. Mery, D., da Silva, R.R., Calôba, L.P., Rebello, J.M.A.: Pattern recognition in the automatic inspection of aluminium castings. Insight Non-Destr. Test. Condition Monit. 45(7), 475–483 (2003). https://doi.org/10.1784/insi.45.7.475.54452

    Article  Google Scholar 

  18. Li, X., Tso, S.K., Guan, X.P., Huang, Q.: Improving automatic detection of defects in castings by applying wavelet technique. IEEE Trans. Ind. Electron. 53(6), 1927–1934 (2006). https://doi.org/10.1109/TIE.2006.885448

    Article  Google Scholar 

  19. Hernández, S., Sáez, D., Mery, D.: Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings. In: A. Campilho, M. Kamel (eds.) Image Analysis and Recognition, Lecture Notes in Computer Science, pp. 826–833. Springer, Berlin, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30126-4-100

  20. Tang, Y., Zhang, X., Li, X., Guan, X.: Application of a new image segmentation method to detection of defects in castings. Int. J. Adv. Manuf. Technol. 43(5), 431–439 (2009). https://doi.org/10.1007/s00170-008-1720-1

    Article  Google Scholar 

  21. Zapata, J., Vilar, R., Ruiz, R.: Automatic inspection system of welding radiographic images based on ANN under a regularisation process. J. Nondestr. Eval. 31(1), 34–45 (2012). https://doi.org/10.1007/s10921-011-0118-4

    Article  Google Scholar 

  22. Mery, D., Arteta, C.: Automatic Defect Recognition in X-Ray Testing Using Computer Vision. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1026–1035 (2017). https://doi.org/10.1109/WACV.2017.119

  23. Niskanen, M., Silven, O., Kauppinen, H.: Color and texture based wood inspection with non-supervised clustering (2001)

  24. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385 [cs] (2015)

  25. Mery, D.: Aluminum casting inspection using deep learning: a method based on convolutional neural networks. J. Nondestr. Eval. 39(1), 12 (2020). https://doi.org/10.1007/s10921-020-0655-9

    Article  Google Scholar 

  26. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. arXiv:1406.2661 [cs, stat] (2014)

  27. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. arXiv:1612.03144 [cs] (2017)

  28. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. arXiv:1409.4842 [cs] (2014)

  29. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (2017)

  30. Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145–151 (1999). https://doi.org/10.1016/S0893-6080(98)00116-6

    Article  Google Scholar 

  31. Ruder, S.: An overview of gradient descent optimization algorithms. arXiv:1609.04747 [cs] (2017)

  32. Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Dollár, P.: Microsoft COCO: common objects in context. arXiv:1405.0312 [cs] (2015)

  33. Yang, R., Wang, R., Deng, Y., Jia, X., Zhang, H.: Rethinking the random cropping data augmentation method used in the training of CNN-based SAR image ship detector. Remote Sens. 13(1), 34 (2021). https://doi.org/10.3390/rs13010034

    Article  Google Scholar 

  34. Takahashi, R., Matsubara, T., Uehara, K.: Data augmentation using random image cropping and patching for deep CNNs. IEEE Trans. Circuits Syst. Video Technol. 30(9), 2917–2931 (2020). https://doi.org/10.1109/TCSVT.2019.2935128

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to Spanish Organisation CDTI (Center for Industrial Technological Development (CDTI)) for the Project JANO (Joint Action towards Digital Transformation) from CIEN Strategic Programme

Author information

Authors and Affiliations

  1. ITP Aero, Alcobendas, Alcobendas, Spain

    A. García Pérez

  2. Dpto. de Arquitectura de Computadores y Automática (DACyA), Universidad Complutense de Madrid (UCM), Madrid, Spain

    M. J. Gómez Silva

  3. Intelligent Systems Lab, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    A. de la Escalera Hueso

Authors
  1. A. García Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. J. Gómez Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. de la Escalera Hueso
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. García Pérez.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

García Pérez, A., Gómez Silva, M.J. & de la Escalera Hueso, A. Automated Defect Recognition of Castings Defects Using Neural Networks. J Nondestruct Eval 41, 11 (2022). https://doi.org/10.1007/s10921-021-00842-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-021-00842-1

Keywords

Search

Navigation