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Bayesian-optimized machine learning framework for predicting Thermal Contact Resistance
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DOI：10.1016/j.mtcomm.2025.114478.png)
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Abstract
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• 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.
Keywords:
Thermal Contact Resistance (TCR)
Machine Learning
Bayesian Optimization
Thermal Management
Predictive Modeling
Cook's Distance
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