Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach

• Published in: Soldering & surface mount technology Vol. 36; no. 2; pp. 69 - 79
• Main Authors: Ruiz-Jacinto, Vicente-Segundo, Gutiérrez-Valverde, Karina-Silvana, Aslla-Quispe, Abrahan-Pablo, Burga-Falla, José-Manuel, Alarcón-Sucasaca, Aldo, Huamán-Romaní, Yersi-Luis
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 20-02-2024; Emerald Group Publishing Limited
• Subjects: Alloys; Continuum damage mechanics; Correlation; Data analysis; Data collection; Ductility; Fatigue life assessment; Finite element method; Heat treating; Investigations; Iterative methods; Life assessment; Low cycle fatigue; Machine learning; Mathematical models; Metal fatigue; Outliers (statistics); Regression analysis; Root-mean-square errors; Simulation; Solders; Stress concentration; Temperature; Thickness; Tin base alloys; Fatigue lifetime; Finite element modeling; Solder alloy; Machine learning
• DOI: 10.1108/SSMT-08-2023-0045
• EISSN: 1758-6836; 0954-0911
• ISSN: 0954-0911

Purpose
This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R2 = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.
Design/methodology/approach
This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R2 = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.
Findings
The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R2” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.
Originality/value
The authors confirm the originality of this paper.