Comparative study of machine learning method and response surface methodology in BGA solder joint parameter optimization
Soldering & Surface Mount Technology
ISSN: 0954-0911
Article publication date: 7 November 2024
Issue publication date: 2 January 2025
Abstract
Purpose
Ball grid array (BGA) package is prone to failure issues in a thermal vibration-coupled environment, such as deformation and fracture of solder joints. To predict the minimum equivalent stress of solder joints more accurately and optimize the solder joint structure, this paper aims to compare the machine learning method with response surface methodology (RSM).
Design/methodology/approach
This paper introduced a machine learning algorithm using Grey Wolf Optimization (GWO) Support Vector Regression (SVR) to optimize solder joint parameters. The solder joint height, spacing, solder pad diameter and thickness were the design variables, and minimizing the equivalent stress of solder joint was the optimization objective. The three dimensional finite element model of the printed circuit board assembly was verified by a modal experiment, and simulations were conducted for 25 groups of models with different parameter combinations. The simulation results were employed to train GWO-SVR to build a mathematical model and were analyzed using RSM to obtain a regression equation. Finally, GWO optimized these two methods.
Findings
The results show that the optimization results of GWO-SVR are closer to the simulation results than those of RSM. The minimum equivalent stress is decreased by 8.528% that of the original solution.
Originality/value
This study demonstrates that GWO-SVR is more precise and effective than RSM in optimizing the design of solder joints.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51775388; Open Fund of Hubei Key Laboratory of Electronic Manufacturing and Packaging Integration (Wuhan University) under Grant No. EMPI2023004; Outstanding Young and Middle-aged Scientific Innovation Team of Colleges and Universities of Hubei Province (No. T2022015); Open Fund of Mining Hydraulic Technology and Equipment Engineering Research Center under grant number MHTE23-R11.
Credit author statement: Fang Liu: Conceptualization, Writing – Review & Editing, Project administration; Zhongwei Duan: Methodology, Software, Writing – Original Draft; Runze Gong: Validation, Investigation; Jiacheng Zhou: Resources, Supervision; Zhi Wu: Formal analysis; Nu Yan: Literature collection.
Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Citation
Liu, F., Duan, Z., Gong, R., Zhou, J., Wu, Z. and Yan, N. (2025), "Comparative study of machine learning method and response surface methodology in BGA solder joint parameter optimization", Soldering & Surface Mount Technology, Vol. 37 No. 1, pp. 25-36. https://doi.org/10.1108/SSMT-06-2024-0026
Publisher
:Emerald Publishing Limited
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