ABSTRACT
The qualification of MAM (Metal Additive Manufacturing) processes remains a major challenge due to the complex thermo-mechanical phenomena involved. The process is driven by a small moving heat source that generates highly localized, transient thermal gradients and induces thermal strains. As these strains are constrained by the surrounding material, residual stresses and warpage develop, causing part distortion or even failure. Accurate modeling is essential for understanding the underlying physics, as well as for reliable process qualification and parameter optimization. However, such simulations are computationally expensive due to the small size of the heat source, which introduces disparate spatial scales, and its continuous motion, which gives rise to equally disparate temporal scales.
The need to simultaneously resolve these scales renders high-fidelity part-scale simulations prohibitively expensive. This thesis contributes to the field of MAM modeling on both the applied and methodological fronts. On the applied side, methods for warpage and stress mitigation are investigated in both DED (Directed Energy Deposition) and LPBF (Laser Powder Bed Fusion) processes, including a novel substrate design strategy for DED that significantly reduces residual stresses, and a modeling framework to capture recoater–induced build failure in LPBF.On the methodological front, the thesis focuses on developing efficient strategies for high-fidelity part-scale simulations of LPBF processes, with particular emphasis on overcoming the disparity of temporal scales.
While AMR (Adaptive Mesh Refinement) has become a popular approach to address the challenge of disparate spatial scales, uniform time stepping remains the standard approach in the field. For centimeter-scale parts, this can require hundreds of millions of time-steps, making such simulations computationally unfeasible. Commonly used strategies to alleviate this issue involve extreme simplifications of the thermal model, such as lumping multiple tracks or layers into a single time-step. Effectively, this eliminates the small scales associated with the moving heat source but compromises the model’s predictive accuracy, requiring additional calibration.
Two methods are proposed to address the temporal-scale disparity without eliminating the underlying small scales: the advected subdomain and a Robin–Robin substepping scheme, both designed to preserve model fidelity while drastically reducing computational cost. The advected subdomain method attaches a moving mesh to the laser. By solving the thermal problem in the reference frame of the heat source, the transient dynamics near the melt pool become quasi-steady, allowing the use of significantly larger time-steps. Substepping divides the domain into regions that evolve with different time-steps: finer steps are applied locally around the moving heat source, while larger steps are used away from it.
The developed Robin-Robin coupling scheme proves robust and ensures mesh-independent convergence between the regions. These methods and their components are systematically evaluated through numerical analysis, benchmarked against standard approaches, and validated against experimental data. Furthermore, they are combined to compound their respective benefits. Together, these contributions advance numerical MAM modeling, thereby improving the computational efficiency of high-fidelity simulations and enabling reliable process qualification and optimization.
PhD Advisors:
- Prof Michele Chiumenti
- Prof Luís Miguel Cervera
PHD CANDIDATE
Mr Mehdi Slimani is a PhD candidate in Structural Analysis at CIMNE’s Solid and Fluid Simulation for Industrial Processes research cluster.