Thermal history prediction in Directed Energy Deposition (DED) remains computationally prohibitive due to evolving adiabatic boundaries. Existing approaches face limitations: numerical models are generally expensive; analytical or semi-analytical formulations apply only to static boundaries; and data-driven surrogates require extensive offline training with limited generalization under dynamic boundary updates. To overcome these issues, we proposed a deposition-agnostic, boundary-aware reduced-order model based on Component Mode Synthesis (CMS) that maintains accuracy under arbitrary deposition patterns during DED processes. The build is partitioned into blocks, with fixed-interface normal modes and constraint modes pre-computed offline and incrementally assembled during arbitrary deposition in online simulation. In addition, a two-level dynamic truncation is adopted, which increases the retained modes near the moving heat source while keeping a lower order elsewhere to maintain the overall reduction efficiency. Under linear thermal assumptions, the method operates within a standard FEM workflow and delivers deposition-agnostic acceleration. Validation on three 2D DED benchmarks (single-layer, five-layer build, and a topology-optimized part) demonstrates relative errors of approximately 1%, computational speedups up to 7.7×, and a reduction of DOF to 9.9% of the FEM baseline. Additionally, the approach preserves the numerical robustness of inactive-element paradigms and shows strong potential for extension into thermo-mechanical simulations, enabling efficient studies of deposition strategies.
Du et al. (Sun,) studied this question.