Machine learning-based reduced order modeling for thermal simulation calibration in wire arc additive manufacturing

Accurate prediction of thermal evolution during Wire Arc Additive Manufacturing (WAAM) is essential for process control and defect reduction. High-fidelity simulations provide detailed temperature fields but are computationally expensive, limiting their use for parameter exploration and real-time optimization. In this work, we develop a simulation-based Reduced Order Model (ROM) that combines Proper Orthogonal Decomposition (POD) with XGBoost regression to efficiently predict thermocouple temperature evolution curves. The ROM is trained on a carefully designed set of 120 high-fidelity snapshots from a representative experimental setup and validated on both withheld snapshots and an independent experimental setup to assess its predictive performance under different experimental conditions. The model accurately reproduces temperature profiles and can predict thermal responses for parameter combinations not present in the training data. Leveraging the ROM’s computational efficiency, we systematically optimize key process parameters to identify configurations that enable simulations to reproduce experimental temperature curves with high fidelity. The results demonstrate that the ROM serves as an efficient calibration-oriented surrogate for high-fidelity simulations, facilitating rapid parameter exploration and optimization while substantially reducing computational cost. Highlights • Developed and validated a physics-based thermal model resolving thermocouple temperature histories of WAAM processes. • Developed a Machine Learning-based Reduced Order Model (ROM) for WAAM thermal predictions, enabling rapid exploration of process parameters to calibrate high-fidelity simulations against experimental temperature curves. • ROM trained on only 120 high-fidelity simulation snapshots, demonstrating efficiency in small-data regimes. • Validated across multiple experimental setups, showing robust predictive performance under varying experimental conditions.