| description abstract | Abstract. Ensuring the vibration reliability of electronic packages is crucial for their long-term operation, especially in high-reliability environments. The research investigates the effects of boundary conditions, component placement, and the number of components on the fatigue lifetime of printed circuit board assemblies (PCBA). While traditional approaches relied on numerical models for estimating fatigue life, the current research utilized advanced machine learning models, extreme gradient boosting (XGBoost), and stochastic gradient boosting (SGB), to enhance accuracy and efficiency. The finite element analysis (FEA) and experimental results were rigorously validated by matching natural frequencies and strain values, confirming the robustness of the models in simulating real-world conditions. The machine learning models demonstrated exceptional performance, with XGBoost and SGB achieving high R2 values of 0.957 and 0.978 and low mean absolute percentage error (MAPE) scores of 0.1587 and 0.1322, respectively. The machine learning predicted and numerically estimated fatigue lifetime was further validated through experimentally resonance-based fatigue analysis. The research reveals that strategic component placement and careful consideration of boundary conditions can significantly improve the fatigue lifetime of PCBA. By integrating Shapley additive explanations (SHAP), the research enhances the interpretability of machine learning models, providing insights that contribute to optimizing the design and performance of electronic packages under vibration loading. | |
