Research article Special Issues

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

  • Received: 31 August 2020 Accepted: 21 October 2020 Published: 28 October 2020
  • Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlin-ear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)- based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial re-gression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

    Citation: Yuquan Meng, Manjunath Rajagopal, Gowtham Kuntumalla, Ricardo Toro, Hanyang Zhao, Ho Chan Chang, Sreenath Sundar, Srinivasa Salapaka, Nenad Miljkovic, Placid Ferreira, Sanjiv Sinha, Chenhui Shao. Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7411-7427. doi: 10.3934/mbe.2020379

    Related Papers:

  • Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlin-ear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)- based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial re-gression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.


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    [1] T. H. Kim, J. Yum, S. J. Hu, J. Spicer, J. A. Abell, Process robustness of single lap ultrasonic welding of thin, dissimilar materials, CIRP Annals, 60 (2001), 17-20.
    [2] W. Cai, Ultrasonic welding of lithium-ion batteries, ASME Press, 03, 2017.
    [3] L. Nong, C. Shao, T. H. Kim, S. J. Hu, Improving process robustness in ultrasonic metal welding of lithium-ion batteries, J. Manuf. Syst., 48 (2018), 45-54. doi: 10.1016/j.jmsy.2018.04.014
    [4] A. Siddiq, E. Ghassemieh, Thermomechanical analyses of ultrasonic welding process using thermal and acoustic softening effects, Mech. Mater., 40 (2008), 982-1000. doi: 10.1016/j.mechmat.2008.06.004
    [5] C. Zhang, D. Chen, A. Luo, Joining 5754 automotive aluminum alloy 2-mm-thick sheets using ultrasonic spot welding, Weld. J., 93 (2014), 131-138.
    [6] Z. Ni, F. Ye, Ultrasonic spot welding of aluminum alloys: A review, J. Manuf. Process, 35 (2018), 580-594. doi: 10.1016/j.jmapro.2018.09.009
    [7] H. P. C. Daniels, Ultrasonic welding, Ultrasonics, 3 (1965), 190-196.
    [8] J. Kim, M. Chiao, L. Lin, Ultrasonic bonding of In/Au and Al/Al for hermetic sealing of MEMS packaging, in Technical Digest. MEMS 2002 IEEE International Conference. Fifteenth IEEE International Conference on Micro Electro Mechanical Systems (Cat. No. 02CH37266), IEEE, (2002), 415-418.
    [9] J. Kim, B. Jeong, M. Chiao, L. Lin, Ultrasonic bonding for MEMS sealing and packaging, IEEE T. Adv. Packag., 32 (2009), 461-467. doi: 10.1109/TADVP.2008.2009927
    [10] S. Kumar, C. Wu, G. Padhy, W. Ding, Application of ultrasonic vibrations in welding and metal processing: A status review, J. Manuf. Process, 26 (2017), 295-322, 2017. doi: 10.1016/j.jmapro.2017.02.027
    [11] S. Krüger, G. Wagner, D. Eifler, Ultrasonic welding of metal/composite joints, Adv. Eng. Mater., 6 (2004), 157-159. doi: 10.1002/adem.200300539
    [12] Y. Meng, D. Peng, Q. Nazir, G. Kuntumalla, M. C Rajagopal, H. C. Chang, et al., Ultrasonic welding of soft polymer and metal: a preliminary study, in International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers, 58752 (2019), V002T03A083.
    [13] L. Xi, M. Banu, S. J. Hu, W. Cai, J. A. Abell, Performance prediction for ultrasonically welded dissimilar materials joints, J. Manuf. Sci. Eng., 139 (2017), 011008. doi: 10.1115/1.4033692
    [14] N. Shen, A. Samanta, H. Ding, W. Cai, Simulating microstructure evolution of battery tabs during ultrasonic welding, J. Manuf. Process, 23 (2016), 306-314. doi: 10.1016/j.jmapro.2016.04.005
    [15] C. Shao, K. Paynabar, T. H. Kim, J. J. Jin, S. J. Hu, J. P. Spicer, et al., Feature selection for manufacturing process monitoring using cross-validation, J. Manuf. Syst., 32 (2013), 550-555. doi: 10.1016/j.jmsy.2013.05.006
    [16] W. Guo, C. Shao, T. H. Kim, S. J. Hu, J. Jin, J. P. Spicer, et al., Online process monitoring with near-zero misdetection for ultrasonic welding of lithium-ion batteries: An integration of univariate and multivariate methods, J. Manuf. Syst., 38 (2016), 141-150. doi: 10.1016/j.jmsy.2016.01.001
    [17] C. Shao, T. H. Kim, S. J. Hu, J. A. Abell, J. P. Spicer, et al., Tool wear monitoring for ultrasonic metal welding of lithium-ion batteries, J. Manuf. Sci. Eng., 138 (2016), 051005. doi: 10.1115/1.4031677
    [18] Z. Ma, Y. Zhang, Characterization of multilayer ultrasonic welding based on the online monitoring of sonotrode displacement, J. Manuf. Process., 54 (2020), 138-147. doi: 10.1016/j.jmapro.2020.03.007
    [19] S. S. Lee, C. Shao, T. H. Kim, S. J. Hu, E. K. Asibu, W. Cai, et al., Characterization of ultrasonic metal welding by correlating online sensor signals with weld attributes, J. Manuf. Sci. Eng., 136 (2014), 051019. doi: 10.1115/1.4028059
    [20] C. Shao, W. Guo, T. H. Kim, J. J. Jin, S. J. Hu, J. P. Spicer, et al., Characterization and monitoring of tool wear in ultrasonic metal welding, In 9th International Workshop on Microfactories, (2014), 161-169.
    [21] Y. Zerehsaz, C. Shao, and J. Jin, Tool wear monitoring in ultrasonic welding using high-order decomposition, J. Intell. Manuf., 30 (2019), 657-669. doi: 10.1007/s10845-016-1272-4
    [22] C. Shao, J. Jin, S. J. Hu, Dynamic sampling design for characterizing spatiotemporal processes in manufacturing, J. Manuf. Sci. Eng., 139 (2017), 101002. doi: 10.1115/1.4036347
    [23] Y. Yang, C. Shao, Spatial interpolation for periodic surfaces in manufacturing using a Bessel additive variogram model, J. Manuf. Sci. Eng., 140 (2018), 061001. doi: 10.1115/1.4039199
    [24] Y. Yang, Y. Zhang, Y. D. Cai, Q. Lu, S. Koric, C. Shao, Hierarchical measurement strategy for cost-effective interpolation of spatiotemporal data in manufacturing, J. Manuf. Syst., 53 (2019), 159-168. doi: 10.1016/j.jmsy.2019.09.009
    [25] R. H. Myers, D. C. Montgomery, C. M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons, 2016.
    [26] H. K. Kansal, S. Singh, P. Kumar, Parametric optimization of powder mixed electrical discharge machining by response surface methodology, J. Mater. Process Tech., 169 (2005), 427-436. doi: 10.1016/j.jmatprotec.2005.03.028
    [27] K. H. Hashmi, G. Zakria, M. B. Raza, S. Khalil, Optimization of process parameters for high speed machining of ti-6al-4v using response surface methodology, Int. J. Adv. Manuf. Tech., 85 (2016), 1847-1856. doi: 10.1007/s00170-015-8057-3
    [28] A. H. Plaine, A. R. Gonzalez, U. F. H. Suhuddin, J. F. Dos Santos, N. G. Alcantara, Process parameter optimization in friction spot welding of AA5754 and Ti6Al4V dissimilar joints using response surface methodology, Int. J. Adv. Manuf. Tech., 85 (2016), 1575-1583. doi: 10.1007/s00170-015-8055-5
    [29] S. Srivastava, R.K. Garg, Process parameter optimization of gas metal arc welding on is: 2062 mild steel using response surface methodology, J. Manuf. Process., 25 (2017), 296-305. doi: 10.1016/j.jmapro.2016.12.016
    [30] D. Zhao, Y. Wang, S. Sheng, Z. Lin, Multi-objective optimal design of small scale resistance spot welding process with principal component analysis and response surface methodology, J. Intell. Manuf., 25 (2014), 1335-1348. doi: 10.1007/s10845-013-0733-2
    [31] X. Li, F. Yan, J. Ma, Z. Chen, X. Wen, Y. Cao, RBF and NSGA-II based EDM process parameters optimization with multiple constraints, Math. Biosci. Eng., 16 (2019), 5788-5803. doi: 10.3934/mbe.2019289
    [32] G. Kuntumalla, Y. Meng, M. Rajagopal, R. Toro, H. Zhao, H. C. Chang, et al., Joining techniques for novel metal polymer hybrid heat exchangers, In ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, 59384 (2019), V02BT02A018.
    [33] G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning, Springer, 2013.
    [34] J. Friedman, T. Hastie, R. Tibshirani, the elements of statistical learning, Springer series in statistics, New York, 2001.
    [35] C. Rasmussen, C. Williams, Gaussian process for machine learning, the MIT Press, 2006.
    [36] A. J Smola, B. Sch?lkopf, A tutorial on support vector regression, in Statistics and Computing, 14 (2004), 199-222.
    [37] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, the MIT Press, 2016.
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