Enhancing PCB assemblies reliability: An adaptive CatBoost-MOA approach integrated with Hu-Washizu variational principle for fatigue life prediction

Fatigue life prediction is crucial in ensuring the reliability and longevity of electronic components, particularly in high-stakes industries such as aerospace and defence, where premature failures can lead to catastrophic consequences, safety risks, and high operational costs. The current research introduces a novel adaptive model for predicting the fatigue lifetime of printed circuit board assemblies (PCBAs) under vibrational loading conditions. The model integrates adaptive versions of the Categorical Boosting (CatBoost) model and the Mother Optimization Algorithm (MOA) with the Hu-Washizu variational principle, addressing the limitations of conventional machine learning models by combining both data-driven and physics-informed components, improving accuracy and efficiency. The adaptive CatBoost-MOA-HuWashizu model significantly outperforms traditional methods, achieving a coefficient of determination (R²) of 0.9982 and a mean absolute percentage error (MAPE) of 0.0832 for single-component PCBs, and an R² of 0.9839 and MAPE of 0.0995 for multicomponent PCBs, demonstrating superior predictive capability for non-linear degradation behaviours under vibrational loading conditions. The integration of MOA optimises hyperparameters by balancing exploration and exploitation, reducing computational load and accelerating convergence, while the Hu-Washizu principle ensures efficient parameter tuning and resource allocation. Comparative results show reduced computational time, with faster convergence and no loss in prediction accuracy. The model’s robustness was validated through cross-validation, yielding an average R² of 0.9001 and an average MAPE of 0.3495 for single-component PCBs, and an average R² of 0.9084 and an average MAPE of 0.3466 for multicomponent PCBs, confirming its generalizability across varied operational scenarios. The proposed novel adaptive model achieves near-perfect alignment between predicted and numerically estimated fatigue lifetimes, with minimal residual errors. The research offers a promising solution for real-time fatigue life estimation in aerospace, defence, and related fields. Future research will expand the model’s scalability to other complex loading conditions and component types to enhance its applicability in broader reliability assessment frameworks.