Methods for consideration of thermal effects in distortion simulation for wire arc additive manufacturing

Wire Arc Additive Manufacturing (WAAM) is a promising metal fabrication technique with a high build-up rate compared to other welding-based additive manufacturing processes and is well-suited for large-scale components. However, thermal effects, residual stress, and distortion present significant challenges to achieving precise geometric accuracy in WAAM parts. This thesis presents a comprehensive study on inherent-strain-based distortion simulation in WAAM, focusing on displacement prediction while accounting for thermal effects. In this study, the conventional and two newly developed inherent strain methods for calculating part distortion are explored. The computational models were created based on the digital geometry, while the activation of welds is based on the actual machine coordinates. Dedicated algorithms were formulated to construct computational models, manage inherent strain in five-axis WAAM processes, and enhance numerical stability and efficiency. Two novel inherent strain methods were introduced: (i) the remelting inherent strain method, which mechanically accounts for substrate remelting, and (ii) the thermo-inherent strain method, which sequentially couples mechanical and thermal simulations while decomposing inherent strain into thermal strain and a calibrated constant strain component. The experimental validation results demonstrate that the proposed advanced simulation methodologies can effectively predict residual stress-induced distortions. These simulations were used in the preprocessing for digital geometry predeformation, minimizing actual/target geometric deviation and enhancing WAAM’s industrial applicability. The predictive models developed in this thesis advance WAAM toward industrial-scale adoption by addressing one of its key challenges: geometric accuracy. Beyond WAAM, the proposed methodologies could be integrated into other additive manufacturing processes, enhancing distortion control and process reliability. Furthermore, these approaches have the potential to be applied in robotic automated welding, improving precision and adaptability in complex fabrication environments.