Skip to main content
SpringerLink
Log in

Periodical monitoring of 3D welds and defects generated from ultrasound scans

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Non-destructive periodic monitoring is crucial to evaluate the state of welded joints in many different types of structures over time. One way to do this is to compare new ultrasound scans of a weld with previous ones, which allows qualified analysts to verify any signs of degradation. Considering the very large number of scans to be analyzed and the rather low number of certified analysts, investigating methods to automate this task becomes very relevant. This study proposes a novel approach that uses a 3D geometric model of welds and their defects, generated directly from the raw ultrasound data, to create a unique signature of each weld in a database, allowing retrieval of previous scans when the same weld is re-scanned. Three experiments were designed to test the recognition and retrieval efficiency using this process. The first experiment is a case study that tests if the algorithm can retrieve a list of similar scans. The second experiment defines the threshold value used to determine whether two scans belong to the same weld. The last experiment is designed to find the configuration of the recognition algorithm that yields the best performance. Validation tests using a database of real industrial 3D scans show that the proposed approach can efficiently recognize different scans of the same weld.

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

Similar content being viewed by others

References

  1. American Society for Nondestructive Testing (2007) Nondestructive testing handbook, third edition: volume 7, ultrasonic testing (UT). American Society for Nondestructive Testing, Inc., United States of America, p 588

  2. Taheri H et al (2019) Investigation of nondestructive testing methods for friction stir welding. Metals 9(6):624

    Article  Google Scholar 

  3. Luo H, Chen QH, Doan NCN, Lin W (2016) Automated identification and characterization of clustered weld defects. In: 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, pp 536–541

  4. Groover MP (2007) Fundamentals of modern manufacturing: materials processes, and systems. John Wiley & Sons

  5. Oh SB, Cheong YM, Lee DH, Kim KM (2017) Development of a magnetostrictive guided wave technique for defect detection and monitoring in a pipe weld. J Korean Soc Nondestruct Test 37(4):230–238

    Article  Google Scholar 

  6. van Sloun RJ, Cohen R, Eldar YC (2019) Deep learning in ultrasound imaging. Proceedings of the IEEE 108(1):11–29

    Article  Google Scholar 

  7. Munir N, Park J, Kim H-J, Song S-J, Kang S-S (2020) Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder. NDT E Int 101:102218

  8. Cantero-Chinchilla S, Wilcox PD, Croxford AJ (2022) Deep learning in automated ultrasonic NDE-developments, axioms and opportunities. NDT & E International 102703

  9. Zhang Z et al (2022) Deep learning-based monitoring of surface residual stress and efficient sensing of AE for laser shock peening. J Mater Process Technol 303:117515

    Article  Google Scholar 

  10. Smoqi Z et al (2022) Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning. J Mater Process Technol 304:117550

    Article  Google Scholar 

  11. Ness KL, Paul A, Sun L, Zhang Z (2022) Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing. J Mater Process Technol 302:117472

    Article  Google Scholar 

  12. Maier M, Kunstmann H, Zwicker R, Rupenyan A, Wegener K (2022) Autonomous and data-efficient optimization of turning processes using expert knowledge and transfer learning. J Mater Process Technol 303:117540

    Article  Google Scholar 

  13. Molitor DA, Kubik C, Becker M, Hetfleisch RH, Lyu F, Groche P (2022) Towards high-performance deep learning models in tool wear classification with generative adversarial networks. J Mater Process Technol 302:117484

    Article  Google Scholar 

  14. Yang J, Lu X, Chen W (2021) A robust scheme for 3D point cloud copy detection. arXiv preprint arXiv 2110.00972

  15. Shukla D, Sharma M (2012) Watermarking schemes for copy protection: a survey. Int J Comput Sci Eng Surv 3(1):65

    Article  Google Scholar 

  16. Medimegh N, Belaid S, Werghi N (2015) A survey of the 3D triangular mesh watermarking techniques. Int J Multimed 1(1)

  17. Cheng SW, Dey TK., Shewchuk J (2012) Delaunay Mesh Generation. CRC Press

  18. Pang L, Lan Y, Guo J, Xu J, Xu J, Cheng X (2017) Deeprank: a new deep architecture for relevance ranking in information retrieval. Association for Computing Machinery, New York, NY. https://doi.org/10.1145/3132847.3132914

  19. Wang J et al (2014) Learning fine-grained image similarity with deep ranking. Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, USA, pp 1386–1393

    Google Scholar 

  20. Sinha P, Russell R (2011) A perceptually based comparison of image similarity metrics. Perception 40(11):1269–1281

    Article  Google Scholar 

  21. Morra L, Lamberti F (2019) Benchmarking unsupervised near-duplicate image detection. Expert Syst Appl 135:313–326

    Article  Google Scholar 

  22. Naumann F, Herschel M (2010) An introduction to duplicate detection. Synth Lect Data Manag 2:1–87

    Article  MATH  Google Scholar 

  23. Liu X, Xu L (2013) Detecting approximately duplicate records in database. In: Proceedings of International Conference on Engineering, Science and Applications (IEA) 2012. Springer, pp 325–332

  24. Xu H, Wang J, Li Z, Zeng G, Li S, Yu N (2011) Complementary hashing for approximate nearest neighbor search. In: 2011 Int Conf Comput Vision, IEEE, pp 1631–1638

  25. Paulevé L, Jégou H, Amsaleg L (2010) Locality sensitive hashing: a comparison of hash function types and querying mechanisms. Pattern Recogn Lett 31:1348–1358

    Article  Google Scholar 

  26. Bentley JL (1975) |Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517

    Article  MATH  Google Scholar 

  27. Ciaccia P, Patella M, Zezula P (1997) M-tree: an efficient access method for similarity search in metric spaces. In Vldb Citeseer 97:426–435

  28. Malkov YA, Yashunin DA (2018) Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans Pattern Anal Mach Intell 42(4):824–836

    Article  Google Scholar 

  29. Aumüller M, Bernhardsson E, Faithfull A (2017) ANN-benchmarks: a benchmarking tool for approximate nearest neighbor algorithms. In: International Conference on Similarity Search and Applications. Springer, pp 34–49

  30. Ke Y, Sukthankar R, Huston L, Ke Y, Sukthankar R (2004) Efficient near-duplicate detection and sub-image retrieval. ACM Int Conf Multimed 4(1):5

    Google Scholar 

  31. Provencal E, Laperrière L (2021) Detection of exact and near duplicates in phased-array ultrasound weld scan. Procedia Manuf 54:263–268

    Article  Google Scholar 

  32. Provencal E, Laperrière L (2022) WeldNet: from 3D phased-array ultrasound scans to 3D geometrical models of welds and defects. CIRP Ann 71(1):445–448

    Article  Google Scholar 

  33. Erler P, Guerrero P, Ohrhallinger S, Mitra NJ, Wimmer M (2020) Points2surf learning implicit surfaces from point clouds. European Conference on Computer Vision. Springer, pp 108–124

    Google Scholar 

  34. Aspert N, Santa-Cruz D, Ebrahimi T (2002) Mesh: measuring errors between surfaces using the hausdorff distance. In Proc IEEE Int Conf Multimed Expo IEEE 1:705–708

  35. Ma J et al (2021) Loss odyssey in medical image segmentation. Med Image Anal 71:102035

  36. Provencal E, Laperrière L (2021) Identification of weld geometry from ultrasound scan data using deep learning. Procedia CIRP 104:122–127

    Article  Google Scholar 

Download references

Funding

This work is supported by the Natural Sciences and Engineering Council of Canada (NSERC), [401221093] and the Mitacs accelerate program [IT15174].

Author information

Authors and Affiliations

  1. Université du Québec À Trois-Rivières, 3351, Boul. Des Forges, Trois-Rivières, Québec, G8Z 4M3, Canada

    Etienne Provencal & Luc Laperrière

Authors
  1. Etienne Provencal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luc Laperrière
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception, design, and analysis. The first draft of the manuscript was written by Etienne Provencal, and then Luc Laperrière commented on and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Etienne Provencal.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Provencal, E., Laperrière, L. Periodical monitoring of 3D welds and defects generated from ultrasound scans. Int J Adv Manuf Technol 125, 1239–1249 (2023). https://doi.org/10.1007/s00170-022-10785-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10785-0

Keywords

Search

Navigation