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Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints

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Abstract

Recently, machine learning algorithms have been extensively utilized in resistance spot welding (RSW) applications to develop non-destructive weldability assessment systems to predict nugget width of RSW joints. However, different predictive models have different prediction performance that can be highly inconsistent. It is critical to compare predictive models and determine the efficient model(s). To the best of our knowledge, a comprehensive analysis and systematic prediction performance comparison of RSW nugget width prediction models have not been performed. This paper presents a statistical performance comparison methodology based on bootstrapping and hypothesis testing techniques to systematically compare the prediction performance of predictive models and determine the efficient model(s). Also, a deep neural net (DNN) nugget width prediction model is developed, analyzed, and compared with prior models. Bootstrapping is applied to generate sampling distributions for each predictive model, and statistical comparison tests are employed to analyze and compare the performance of each predictive model and identify statistically significant performance differences. Results of this analysis indicate that DNN, developed for RSW nugget width prediction in this paper, outperforms previous models.

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References

  1. Hamidinejad SM, Kolahan F, Kokabi AH (2012) The modeling and process analysis of resistance spot welding on galvanized steel sheets used in car body manufacturing. Mater Des 34:759–767

    Google Scholar 

  2. Ahmed F, Kim KY (2017) Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manuf 10:1009–1019

    Google Scholar 

  3. Raoelison R, Fuentes A, Rogeon P, Carre P, Loulou T, Carron D, Dechalotte F (2012) Contact conditions on nugget development during resistance spot welding of Zn coated steel sheets using rounded tip electrodes. J Mater Process Technol 212:1663–1669

    Google Scholar 

  4. Moshayedi H, Sattari-Far I (2012) Numerical and experimental study of nugget size growth in resistance spot welding of austenitic stainless steels. J Mater Process Technol 212:347–354

    Google Scholar 

  5. Martin Ó, Lopez M, De Tiedra P, San Juan M (2008) Prediction of magnetic interference from resistance spot welding processes on implantable cardioverter-defibrillators. J Mater Process Technol 206:256–262

    Google Scholar 

  6. Andersson O, Melander A (2015) Prediction and verification of resistance spot welding results of ultra-high strength steels through FE simulations. Model Numer Simul Mater Sci 5:26–37

    Google Scholar 

  7. Arunchai T, Sonthipermpoon K, Apichayakul P, Tamee K (2014) Resistance spot welding optimization based on artificial neural network. Int J Manuf Eng 2014:1–6

    Google Scholar 

  8. El Ouafi A, Bélanger R, Méthot JF (2011) Artificial neural network-based resistance spot welding quality assessment system. Rev Métall Int J Metall 108:343–355

    Google Scholar 

  9. Kim KY, Park J, Sohmshetty R (2017) Prediction measurement with mean acceptable error for proper inconsistency in noisy weldability prediction data. Robot Comput Integr Manuf 43:18–29

    Google Scholar 

  10. Li W, Hu SJ, Ni J (2000) On-line quality estimation in resistance spot welding. J Manuf Sci Eng 122:511–512

    Google Scholar 

  11. Ivezic N, Alien JD, Zacharia T (1999) Neural network-based resistance spot welding control and quality prediction. In: Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials (IPMM’99), pp 989–994

    Google Scholar 

  12. Tran HT, Yang HJ, Kim KY, Sohmshetty R (2015) A comparative study for weldability prediction of AHSS stackups. Int J Multimed Ubiquit Eng 10:265–278

    Google Scholar 

  13. Sumesh A, Rameshkumar K, Mohandas K, Babu RS (2015) Use of machine learning algorithms for weld quality monitoring using acoustic signature. Procedia Comput Sci 50:316–322

    Google Scholar 

  14. Moradi-Aliabadi M, Huang Y (2016) Multistage optimization for chemical process sustainability enhancement under uncertainty. ACS Sustain Chem Eng 4:6133–6143

    Google Scholar 

  15. Gavidel SZ, Rickli JL (2017) Quality assessment of used-products under uncertain age and usage conditions. Int J Prod Res 55:7153–7167

    Google Scholar 

  16. Nezhad MZ, Zhu D, Li X, Yang K, Levy P (2016) Safs: a deep feature selection approach for precision medicine. In: Proceedings of the international conference on bioinformatics and biomedicine (BIBM), pp 501–506

    Google Scholar 

  17. Gavidel SZ, Rickli JL (2015) Triage as a core sorting strategy in extreme core arrival scenarios. J Remanuf 5:9

    Google Scholar 

  18. Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection using support vector machine learning in a milling process. Int J Mach Tools Manuf 241:249

    Google Scholar 

  19. Kadirgama K, Noor MM, Rahman MM (2012) Optimization of surface roughness in end milling using potential support vector machine. Arab J Sci Eng 2269:2275

    Google Scholar 

  20. Liu Y, Wang C (1999) Neural network based adaptive control and optimization in the milling process. Int J Adv Manuf Technol 791:795

    Google Scholar 

  21. Pal S, Pal SK, Samantaray AK (2008) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater Process Technol 202:464–474

    Google Scholar 

  22. Brown JD, Rodd MG, Williams NT (1998) Application of artificial intelligence techniques to resistance spot welding. Ironmak Steelmak 199:207

    Google Scholar 

  23. Laurinen P, Junno H, Tuovinen L, Röning J (2004) Studying the quality of resistance spot welding joints using bayesian networks. In: Proceedings of artificial intelligence and applications, vol 705, p 711

    Google Scholar 

  24. Pereda, María JI, Santos, Óscar Martín, Galán JM (2015) Direct quality prediction in resistance spot welding process: sensitivity, specificity and predictive accuracy comparative analysis. Sci Technol Weld Join 679: 685

  25. Summerville C, Adams D, Compston P, Doolan M (2017) Nugget diameter in resistance spot welding: a comparison between a dynamic resistance based approach and ultrasound C-scan. Procedia Eng 257:263

    Google Scholar 

  26. Junno H, Laurinen P, Haapalainen E, Tuovinen L, Roning J (2005) Resistance spot welding process identification using an extended knn method. In: Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE), pp 7–12

    Google Scholar 

  27. Breiman L (2001) Random forests. Mach Learn 45:5–32

    MATH  Google Scholar 

  28. Ho TK (1995) Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, pp 278–282

    Google Scholar 

  29. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  30. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185

    MathSciNet  Google Scholar 

  31. Cleary JG, Trigg LE (1995) K*: an instance-based learner using an entropic distance measure. In: Proceedings of machine learning, pp 108–114

    Google Scholar 

  32. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    MathSciNet  Google Scholar 

  33. Hastie T, Tibshirani R (1987) Generalized additive models: some applications. J Am Stat Assoc 398:371–386

    MATH  Google Scholar 

  34. Nelder JA, Baker RJ (2004) Generalized linear models. Encyclopedia of statistical sciences, p 4

    Google Scholar 

  35. Bengio Y (2009) Learning deep architectures for AI. Foundations and trends® in. Mach Learn 2:1–127

    MATH  Google Scholar 

  36. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error in assessing average model performance. Clim Res 30:79–82

    Google Scholar 

  37. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, New York

    MATH  Google Scholar 

  38. Montgomery DC (2017) Design and analysis of experiments. Wiley

  39. Levene H (1961) Robust tests for equality of variances. Contributions to probability and statistics. In: Essays in honor of Harold Hotelling, pp 279–292

    Google Scholar 

  40. Wiley JF (2016) R deep learning essentials. Packt Publishing Ltd.

  41. Ciresan D, Giusti A, Gambardella LM, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in neural information processing systems, pp 2843–2851

    Google Scholar 

  42. Pomerleau DA (1991) Efficient training of artificial neural networks for autonomous navigation. Neural Comput 3:88–97

    Google Scholar 

  43. Van Gerven M, Bohte S (2018) Artificial neural networks as models of neural information processing. Front Media, SA

  44. Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR

  45. Christopher MB (2016) Pattern recognition & machine learning. Springer-Verlag, NewYork

    Google Scholar 

  46. Alpaydin E (2009) Introduction to machine learning. MIT press

  47. Bengio Y, Goodfellow IJ, Courville A (2015) Deep learning. Nature 7553:436–444

    MATH  Google Scholar 

  48. Nielsen MA (2015) Neural networks and deep learning. Determination Press

  49. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  MATH  Google Scholar 

  50. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. HotCloud 10:95

    Google Scholar 

  51. De Bièvre P, (2009) The 2007 international vocabulary of metrology (VIM), JCGM 200: 2008 [ISO/IEC guide 99]: meeting the need for intercontinentally understood concepts and their associated intercontinentally agreed terms. Clin Biochem 42: 246–248

  52. Snedecor GWC, William G (1989) Statistical methods no. QA276. 12. S6313

  53. Montgomery DC, Runger GC, Hubele NF (2009) Engineering statistics. Wiley

  54. Tiwari A, Hihara L, Rawlins J (2014) Intelligent coatings for corrosion control. Butterworth-Heinemann

  55. Rice J (2006) Mathematical statistics and data analysis. Beijing, China

  56. Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining. Pearson

  57. Gedeon TD (1997) Data mining of inputs: analysing magnitude and functional measures. Int J Neural Syst 8:209–218

    Google Scholar 

  58. Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28:459–471

    Google Scholar 

  59. Hall MA (1999) Correlation-based feature selection for machine learning. Dissertation, the University of Waikato

    Google Scholar 

  60. Dash M, Liu H, Motoda H (2000) Consistency based feature selection. In: Proceedings of Pacific-Asia conference on knowledge discovery and data mining, pp 98–109

    Google Scholar 

  61. Archer KJ, Kimes RV (2008) Empirical characterization of random forest variable importance measures. Comput Stat Data Anal 52:2249–2260

    MathSciNet  MATH  Google Scholar 

  62. Lei S (2012). A feature selection method based on information gain and genetic algorithm. In Proceedings of International Conference on Computer Science and Electronics Engineering (ICCSEE), pp. 355–358

  63. Karegowda AG, Manjunath AS, Jayaram MA (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inform Technol Knowl Manag 2:271–277

    Google Scholar 

  64. Kannan SS, Ramaraj N (2010) A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm. Knowl-Based Syst 23:580–585

    Google Scholar 

  65. Aslanlar S (2006) The effect of nucleus size on mechanical properties in electrical resistance spot welding of sheets used in automotive industry. Mater Des 27:125–131

    Google Scholar 

  66. Mirzaei F, Ghorbani H, Kolahan F (2017) Numerical modeling and optimization of joint strength in resistance spot welding of galvanized steel sheets. Int J Adv Manuf Technol 92:3489–3501

    Google Scholar 

  67. Raut M, Achwal V (2014) Optimization of spot welding process parameters for maximum tensile strength. Int J Mech Eng Robot Res 3:506–517

    Google Scholar 

  68. Zhang W, Sun D, Han L, Li Y (2015) Optimised design of electrode morphology for novel dissimilar resistance spot welding of aluminium alloy and galvanised high strength steel. Mater Des 85:461–470

    Google Scholar 

  69. Scholz FW, Stephens MA (1987) K-sample Anderson–Darling tests. J Am Stat Assoc 918:924

    MathSciNet  Google Scholar 

  70. Posten HO (1984) Robustness of the two-sample t-test. In: Robustness of statistical methods and nonparametric statistics. Springer, Dordrecht, pp 92–99

    Google Scholar 

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Funding

This research is based upon work supported by the Digital Manufacturing and Design Innovation Institute (DMDII) under grant DMDII-15-07-04.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Wayne State University, 4815 Fourth Street, Rm. 1062, Detroit, MI, 48202, USA

    Saeed Zamanzad Gavidel & Jeremy L. Rickli

  2. Department of Computer Science, Big Data Research Laboratory, Wayne State University, 5057 Woodward Avenue, Detroit, MI, 48202, USA

    Saeed Zamanzad Gavidel & Shiyong Lu

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  1. Saeed Zamanzad Gavidel
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Correspondence to Saeed Zamanzad Gavidel.

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Zamanzad Gavidel, S., Lu, S. & Rickli, J.L. Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints. Int J Adv Manuf Technol 105, 3779–3796 (2019). https://doi.org/10.1007/s00170-019-03821-z

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