Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology
Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology
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DOI：
10.3934/mbe.2020379
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发表时间：
2020-01-01
影响因子：
2.6
通讯作者：
Shao, Chenhui
中科院分区：
文献类型：
--
作者：
Meng, Yuquan;Rajagopal, Manjunath;Shao, Chenhui
超声波金属焊接（UMW）是一种固态连接技术，具有多种工业应用。尽管UMW具有许多优点，但其操作窗口相对较窄，并且对工艺条件的变化敏感。因此，必须定量表征焊接参数对接头质量的影响。量化模型随后可用于优化参数。传统的响应面法通常采用线性或多项式模型，这可能无法捕捉复杂的，非线性的输入输出关系的UMW。此外，一些UMW应用要求同时优化多个质量指标，如剥离强度、剪切强度、导电性和导热性。为了应对这些挑战，本文开发了一种基于机器学习（ML）的响应面模型，对UMW的输入输出关系进行建模，并联合优化两个质量指标，即剥离强度和剪切强度。各种ML方法，包括样条回归，高斯过程回归（GPR），支持向量回归（SVR），和传统的多项式回归模型的性能进行了比较。实验结果表明，基于径向基函数（RBF）核的GPR和基于RBF核的SVR具有最佳的预测精度。然后使用所获得的响应面模型来优化复合接头强度指标，该指标被定义为归一化剪切强度和剥离强度的平均值。此外，案例研究揭示了不同的模式，在响应面的剪切和剥离强度，这还没有在文献中进行了系统的研究。虽然开发的UMW应用程序，该方法可以扩展到其他制造工艺。
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, nonlinear 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 regression 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.