Bayesian-optimized machine learning framework for predicting Thermal Contact Resistance

Accurate prediction of Thermal Contact Resistance (TCR) is essential for optimizing thermal management—whether enhancing or limiting heat flow—in energy, electronics, and aerospace systems. This study develops a robust data-driven framework that integrates Bayesian Optimization (BO) with machine learning (ML) to predict TCR across diverse interstitial materials and operating conditions, using a curated dataset of 300 experimental points. The analysis revealed a clear physical hierarchy: metal/solder interfaces exhibited the lowest resistance (≈8.3 ×10⁻⁶ K·m²/W), while the bare interface showed the highest (≈4.8 ×10⁻² K·m²/W). After dataset refinement using Cook’s Distance, the optimized ML model achieved a high coefficient of determination (R² = 0.898), outperforming the standard ML model (R² = 0.828). Feature importance analysis identified the interstitial material as the most dominant predictor, with an importance score more than twice that of any other feature, underscoring its pivotal role in governing heat transfer. Contact pressure ranked second, followed by the thermal conductivity of the first material, while other features exerted minor influence. Although the BO process did not further reduce the cross-validated RMSE, the final framework proved robust, stable, and physically consistent, providing a reliable foundation for data-driven prediction of TCR. Besides, the data-driven framework exhibits higher predictive accuracy than the classical model. • Bayesian-optimized machine learning framework developed to predict Thermal Contact Resistance (TCR). • Cook’s Distance filtering improved data quality and increased model accuracy. • Interstitial material identified as the main factor affecting TCR. • Contact pressure identified as the second major factor influencing TCR. • Framework enables efficient and scalable thermal management system design.