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An Original Machine Learning-Based Approach for the Online Monitoring of Refill Friction Stir Spot Welding: Weld Diagnostic and Tool State Prognostic

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Abstract

The process monitoring (PM) of refill friction stir spot welding (Refill FSSW) can play a substantial role in detecting various issues, especially defects in the spot being formed and the tool state degradation, which allows in time intervention to improve the welding process. Since Refill FSSW is somewhat an emergent technology, PM has received scarce attention. In this paper, the performance of PM using acoustic emission (AE) technique is studied for two purposes: detecting defects in weld while being formed and predicting the tool state. To do so, the common defects that can occur during the process were first intentionally created and monitored using AE. The corresponding collected data have served then as an input for two defect detection models. The first one is based on novelty detection and has shown an average classification performance. The second, which shows higher performance, uses multi-class classification algorithms. Concerning the tool state, a novel state index was developed to predict when the process must be stopped in order to clean the tool and avoid hence related weld defects and tool fracture.

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References

  1. A.M. Nasiri, Z. Shen, J.S.C. Hou, and A.P. Gerlich, Failure Analysis of Tool Used in Refill Friction Stir Spot Welding of Al 2099 Alloy, Eng. Fail. Anal., 2018, 84, p 2533.

    Article  CAS  Google Scholar 

  2. Z. Shen, Y. Chen, J.S.C. Hou, X. Yang, A.P. Gerlich, Influence of processing parameters on microstructure and mechanical performance of refill friction stir spot welded 7075–T6 aluminium alloy. Sci. Technol. Weld. Join., 2015, 20(1), p 48–57.

    Article  CAS  Google Scholar 

  3. C. Schilling and J. dos Santos, U.S. Patent No. 6,722,556B2 (2002). Method and and device for joining at least two adjoining work pieces by friction welding. https://patents.google.com/patent/US6722556B2/en

  4. U. Suhuddin, R. Mesquita, and J.F. Santos, Friction Spot Welding of Similar Aluminum Alloys AA6082-T6 for Application in Automotive Industry. Int. Automot. Congr.-IABC. (2016)

  5. E. Boldsaikhan, S. Fukada, M. Fujimoto, K. Kamimuki, and H. Okada, Refill Friction Stir Spot Welding of Surface-Treated Aerospace Aluminum Alloys with Faying-Surface Sealant, J. Manuf. Process., 2019, 42, p 113–120.

    Article  Google Scholar 

  6. I. Kwee, W. De Waele, and K. Faes, Weldability of High-Strength Aluminium Alloy EN AW-7475-T761 Sheets for Aerospace Applications, Using Refill Friction Stir Spot Welding, Weld. World, 2019, 63(4), p 1001–1011.

    Article  CAS  Google Scholar 

  7. J.Y. Cao, M. Wang, L. Kong, H.X. Zhao, and P. Chai, Microstructure, Texture and Mechanical Properties During Refill Friction Stir Spot Welding of 6061–T6 Alloy, Mater. Charact., 2017, 128, p 54–62.

    Article  CAS  Google Scholar 

  8. B.H. Silva, G. Zepon, C. Bolfarini, and J.F. dos Santos, Refill Friction Stir Spot Welding of AA6082-T6 Alloy: Hook Defect Formation and Its Influence on the Mechanical Properties and Fracture Behavior, Mater. Sci. Eng. A, 2020, 773, p 138724.

    Article  CAS  Google Scholar 

  9. C.C. de Castro, A.H. Plaine, G.P. Dias, N.G. de Alcântara, and J.F. dos Santos, Investigation of Geometrical Features on Mechanical Properties of AA2198 Refill Friction Stir Spot Welds, J. Manuf. Process., 2018, 36, p 330–339.

    Article  Google Scholar 

  10. U.F.H. Suhuddin, V. Fischer, A. Kostka, and J.F. Dos Santos, Microstructure Evolution in Refill Friction Stir Spot Weld of A Dissimilar Al-Mg Alloy to Zn-Coated Steel, Sci. Technol. Weld. Join., 2017, 22(8), p 658–665.

    Article  CAS  Google Scholar 

  11. Y.Q. Zhao, H.J. Liu, S.X. Chen, Z. Lin, and J.C. Hou, Effects of Sleeve Plunge Depth on Microstructures and Mechanical Properties of Friction Spot Welded Alclad 7B04-T74 Aluminum Alloy, Mater. Des., 2014, 1980–2015(62), p 40–46.

    Article  Google Scholar 

  12. A.C. Ferreira, L.C. Campanelli, U.F.H. Suhuddin, N.G. de Alcântara, and J.F. dos Santos, Investigation of Internal Defects and Premature Fracture of Dissimilar Refill Friction Stir Spot Welds of AA5754 and AA6061, Int. J. Adv. Manuf. Technol., 2020, 106(7), p 3523–3531.

    Article  Google Scholar 

  13. A. Kubit and T. Trzepiecinski, A Fully Coupled Thermo-Mechanical Numerical Modelling of the Refill Friction Stir Spot Welding Process in Alclad 7075–T6 Aluminium Alloy Sheets, Arch. Civ. Mech. Eng., 2020, 20(4), p 1–14.

    Article  Google Scholar 

  14. R.C. Brzostek, U. Suhuddin, and J.F. Dos Santos, Fatigue Assessment of Refill Friction Stir Spot Weld in AA 2024–T3 Similar Joints, Fatigue Fract. Eng. Mater. Struct., 2018, 41(5), p 1208–1223.

    Article  Google Scholar 

  15. Z. Xu, Z. Li, S. Ji, and L. Zhang, Refill Friction Stir Spot Welding of 5083-O Aluminum Alloy, J. Mater. Sci. Technol., 2018, 34(5), p 878–885.

    Article  CAS  Google Scholar 

  16. C. Schmal, G. Meschut, and N. Buhl, Joining of High Strength Aluminum Alloys by Refill Friction Stir Spot Welding (III-1854-18), Weld. World, 2019, 63(2), p 541–550.

    Article  CAS  Google Scholar 

  17. SIST EN 1330–9: 2017-Non-destructive testing - Terminology - Part 9: Terms used in acoustic emission testing. https://standards.iteh.ai/catalog/standards/cen/a4599dc9-83ab-41f5-9afa-07ac2ea9f8db/en-1330-9-2017

  18. ASTM E751/E751M-17: Standard practice for acoustic emission monitoring during resistance spot-welding. https://www.boutique.afnor.org/fr-fr/norme/astm-e751-e751m17//am97528/275744

  19. K. Dudzik, The Possibility of Application Acoustic Emission Method to Optimize Determination of Finish Lathing Parameters, J. Kones, 2015, 22(3), p 33–39.

    Google Scholar 

  20. C.N. Suresha, B.M. Rajaprakash, and S. Upadhya, Applicability of Acoustic Emission in the Analysis of Friction Stir Welded Joints, Int. J. Recent Trends Eng., 2009, 1(5), p 86.

    Google Scholar 

  21. S.K. Oh, A. Hasui, T. Kunio, and KWang, K., Effects of Initial Energy on Acoustic Emission Relating to Weld Strength in Friction Welding, Trans. JWS, 1982, 13(2), p 24–26.

    Google Scholar 

  22. C. Chen, R. Kovacevic, and D. Jandgric, Wavelet Transform Analysis of Acoustic Emission in Monitoring Friction Stir Welding of 6061 Aluminum, Int. J. Mach. Tools Manuf, 2003, 43(13), p 1383–1390.

    Article  Google Scholar 

  23. A. Levikhina, Nondestructive Online Testing Method for Friction Stir Welding using acoustic emission. in AIP Conference Proceedings (Vol 1909, No. 1, p. 020116). AIP Publishing LLC 2017

  24. D. Ambrosio, G. Dessein, V. Wagner, M. Yahiaoui, J.Y. Paris, M. Fazzini, and O. Cahuc, On the Potential Applications of Acoustic Emission in Friction Stir Welding, J. Manuf. Process., 2022, 75, p 461–475.

    Article  Google Scholar 

  25. W.D. Jolly, Use of Acoustic Emission as a Weld Quality Monitor (No. BNWL-SA-2727; CONF-690962–1). Battelle-Northwest, Richland, Wash. Pacific Northwest Lab. (1969)

  26. S. Charunetratsamee, B. Poopat, and C. Jirarungsatean, Feasibility Study of Acoustic Emission Monitoring of Hot Cracking in GTAW Weld. In Key Engineering Materials (Vol. 545, pp. 236–240). Trans Tech Publications Ltd. (2013)

  27. S. Yaacoubi, F. Dahmene, M. El Mountassir, and A.E. Bouzenad, A Novel AE Algorithm-Based Approach for the Detection of Cracks in Spot Welding in View of Online Monitoring: Case Study, Int. J. Adv. Manuf. Technol., 2021, 117(5), p 1807–1824.

    Article  Google Scholar 

  28. F. Dahmene, S. Yaacoubi, M.E. Mountassir, A.E. Bouzenad, P. Rabaey, M. Masmoudi, and A. Taram, On the Nondestructive Testing and Monitoring of Cracks in Resistance Spot Welds: Recent Gained Experience, Weld. World, 2022, 66(4), p 629–641.

    Article  Google Scholar 

  29. W.M. Zeng, H.L. Wu, and J. Zhang, Effect of Tool Wear on Microstructure, Mechanical Properties and Acoustic Emission of Friction Stir Welded 6061 Al Alloy, Acta Metallurgica Sinica (English Lett.), 2006, 19(1), p 9–19.

    Article  Google Scholar 

  30. F. Al-Badour, A. Mahgoub, A. Bazoune, A. Shuaib, and N. Merah, On-Line Condition Monitoring of Friction Stir Spot Welding Tool Using Vibration Measurements. in Pressure Vessels and Piping Conference (vol. 57991, p. V06AT06A023). American Society of Mechanical Engineers. (2017)

  31. L. Zuo, D. Zuo, Y. Zhu, and H. Wang, Acoustic Emission Analysis for Tool Wear State During Friction Stir Joining of SiCp/Al Composite, Int. J. Adv. Manuf. Technol., 2018, 99(5), p 1361–1368.

    Article  Google Scholar 

  32. K. Balachandar and R. Jegadeeshwaran, Friction Stir Welding Tool Condition Monitoring Using Vibration Signals and Random Forest Algorithm–A Machine Learning Approach, Mater. Today Proc., 2021, 46, p 1174–1180.

    Article  CAS  Google Scholar 

  33. T. Montag, J.P. Wulfsberg, H. Hameister, and R. Marschner, Influence of Tool Wear on Quality Criteria for Refill Friction Stir Spot Welding (RFSSW) process, Procedia Cirp, 2014, 24, p 108–113.

    Article  Google Scholar 

  34. N. Morizet, N. Godin, J. Tang, E. Maillet, M. Fregonese, and B. Normand, Classification of Acoustic Emission Signals Using Wavelets and RANDOM Forests: Application to Localized Corrosion, Mech. Syst. Signal Process., 2016, 70, p 1026–1037.

    Article  ADS  Google Scholar 

  35. T.J. Saravanan, N. Gopalakrishnan, and N.P. Rao, Damage Detection in Structural Element Through Propagating Waves Using Radially Weighted and Factored RMS, Measurement, 2015, 73, p 520–538.

    Article  ADS  Google Scholar 

  36. D. D’Angela and M. Ercolino, Acoustic Emission Entropy: An Innovative Approach for Structural Health Monitoring of Fracture-Critical Metallic Components Subjected to Fatigue Loading, Fatigue Fract. Eng. Mater. Struct., 2021, 44(4), p 1041–1058.

    Article  Google Scholar 

  37. S. Chen, C. Yang, G. Wang, and W. Liu, Similarity Assessment of Acoustic Emission Signals and Its Application in Source Localization, Ultrasonics, 2017, 75, p 36–45.

    Article  PubMed  Google Scholar 

  38. P. Senin, Dynamic Time Warping Algorithm Review Information and Computer Science Department University of Hawaii, Technical Report, Manoa Honolulu, 2008.

    Google Scholar 

  39. H.W. Lilliefors, On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown, J. Am. Stat. Assoc., 1967, 62(318), p 399–402.

    Article  Google Scholar 

  40. M. Hofmann, Support Vector Machines-Kernels and the Kernel Trick, Notes, 2006, 26(3), p 1–16.

    Google Scholar 

  41. Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines.

  42. S.I. Amari and S. Wu, Improving Support Vector Machine Classifiers by Modifying Kernel Functions, Neural Netw., 1999, 12(6), p 783–789.

    Article  CAS  PubMed  Google Scholar 

  43. E. Fix and J.L. Hodges, Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties, Int. Stat. Rev., 1989, 57(3), p 238–247.

    Article  Google Scholar 

  44. D.W. Aha Ed., Lazy Learning. Springer, Berlin, 2013

    Google Scholar 

  45. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees, Routledge, Oxford, 2017.

    Book  Google Scholar 

  46. T. Hastie, R. Tibshirani, J.H. Friedman, and J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2009.

    Book  Google Scholar 

  47. R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems, Ann. Eugen., 1936, 7, p 179–188.

    Article  Google Scholar 

  48. C.R. Rao, The Utilization of Multiple Measurements in Problems of Biological Classification, J. Royal Stat. Soc. Ser. B (Methodol.), 1948, 10(2), p 159–203.

    MathSciNet  Google Scholar 

  49. B.L. Welch, Note on Discriminant Functions, Biometrika, 1939, 31, p 218–220.

    MathSciNet  Google Scholar 

  50. T.T. Wong, Performance Evaluation of Classification Algorithms by k-Fold and Leave-One-Out Cross Validation, Pattern Recogn., 2015, 48(9), p 2839–2846.

    Article  ADS  Google Scholar 

  51. Giesteira, F. A. G. (2018). Refill Friction Stir Spot Welding of Casting AM50A Mg Alloy to Zn Coated DP600 Steel Dissimilar Joints.

  52. A. Marec, J.H. Thomas, and R. El Guerjouma, Damage Characterization of Polymer-Based Composite Materials: Multivariable Analysis and Wavelet Transform for Clustering Acoustic Emission Data, Mech. Syst. Signal Process., 2008, 22(6), p 1441–1464.

    Article  ADS  Google Scholar 

  53. A.H. Najmi and J. Sadowsky, The Continuous Wavelet Transform and Variable Resolution Time-Frequency Analysis, J. Hopkins APL Tech. Dig., 1997, 18(1), p 134–140.

    Google Scholar 

  54. H. Suzuki, T. Kinjo, Y. Hayashi, M. Takemoto, K. Ono, and Y. Hayashi, Wavelet Transform of Acoustic Emission Signals, J. Acoust. Emiss., 1996, 14, p 69–84.

    CAS  Google Scholar 

  55. M. El Mountassir, S. Yaacoubi, G. Mourot, and D. Maquin, An Adaptive PCA-Based Method for More Reliable Ultrasonic Guided Waves SHM: Data-Driven Modeling and Experimental Validation in High Attenuating Medium, Struct. Control. Health Monit., 2021, 28(1), p e2634.

    Article  Google Scholar 

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Acknowledgments

This work has been conducted within the scope of the European Project DAHLIAS (Development and Application of Hybrid Joining in Lightweight Integral Aircraft Structures). This project is funded by European Union’s HORIZON 2020 framework program, Clean Sky 2 Joint Undertaking, and AIRFRAME ITD under grant agreement No 821081.

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Authors and Affiliations

  1. Equipe Monitoring Et Intelligence Artificielle, Institut de Soudure, 4 Boulevard Henri Becquerel, 57970, Yutz, France

    Fethi Dahmene, Slah Yaacoubi, Mahjoub El Mountassir & Mohamed Masmoudi

  2. Equipe CND Avancés, Institut de Soudure, 4 Boulevard Henri Becquerel, 57970, Yutz, France

    Gaëlle Porot & Pascal Nennig

  3. Institute of Materials Research, Materials Mechanics, Solid State Joining Processes, Helmholtz-Zentrum Hereon, Max-Planck-St.1, 21502, Geesthacht, Germany

    Uceu Fuad Hasan Suhuddin & Jorge Fernandez Dos Santos

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  1. Fethi Dahmene
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  2. Slah Yaacoubi
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  3. Mahjoub El Mountassir
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  4. Gaëlle Porot
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  7. Uceu Fuad Hasan Suhuddin
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Correspondence to Fethi Dahmene or Slah Yaacoubi.

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Dahmene, F., Yaacoubi, S., El Mountassir, M. et al. An Original Machine Learning-Based Approach for the Online Monitoring of Refill Friction Stir Spot Welding: Weld Diagnostic and Tool State Prognostic. J. of Materi Eng and Perform 33, 1931–1947 (2024). https://doi.org/10.1007/s11665-023-08102-1

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