Low-Cycle Fatigue Behavior of Sn-0.3Ag-0.7Cu-0.5CeO2 Composite Solder Alloy

Li, L.; Li, Z. H.; Tang, Yu; Li, G. Y.

doi:10.1007/s11664-022-09958-0

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…By using a custom test stand and in situ resistance monitoring, their study focused on rapid testing times, cost-effectiveness and accurate assessment of solder interconnects' fatigue life through quantifying the plastic work density prior to failure. Li et al (2022) investigated the LCF behavior of an Sn-Ag-Cu-CeO 2 composite solder alloy, finding that the fatigue life exponent and material ductility coefficient were influenced by temperature and frequency. Long et al (2021) studied the LCF damage of SAC305 solder used in electronic packaging.…”

Section: Introductionmentioning

confidence: 99%

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

Ruiz-Jacinto,

Gutiérrez-Valverde,

Aslla-Quispe

et al. 2023

SSMT

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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.

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Section: Introductionmentioning

confidence: 99%