Artificial Intelligence for material property prediction of brazed ceramic-metal assemblies

Multiple AI algorithms, including Linear Regression (LR), Polynomial Regression (PR), Random Forest (RF), Artificial Neural Network (ANN), and a multi-output auto-encoder (AE) model, are developed. The models are tested using k-fold (five-fold) validation. Eight input-output feature configurations are evaluated to predict the single and multi-output parameters. The input-output feature comprises material properties, namely, the coefficient of thermal expansion and Young's modulus of brazed ceramic-metal composite materials obtained from literature, the strength parameter (Von Mises Stress) estimated from Finite Element Method simulation for joint assembly structure containing porosity, and effective CTE value, .