Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach
Vicente-Segundo Ruiz-Jacinto,
Karina-Silvana Gutiérrez-Valverde,
Abrahan-Pablo Aslla-Quispe
et al.
Abstract: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 r… Show more
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