Bayesian Optimization-Based Parameter Identification for Discrete Element Method Simulation of Consolidation and Its Application to Powder Spreading Analysis

This study develops a Bayesian optimization framework to calibrate two discrete element method (DEM) parameters—the cohesion-related surface energy coefficient (k) and the rolling resistance coefficient (μr)—based on experimental void ratio data obtained from powder consolidation tests. The optimized parameter set reproduces the void ratio obtained from the consolidation experiment, demonstrating efficient and physically plausible calibration under confined loading. However, when these parameters are applied to powder spreading simulations, the resulting powder beds become excessively cohesive, leading to poor layer uniformity. This discrepancy is attributed to (i) the mismatch in mechanical scaling (σ/E) between the experimental and simulated conditions and (ii) the shift in dominant particle-scale mechanics from normal-force-controlled consolidation to shear-dominated spreading. The results indicate that DEM parameter calibration for powder bed-based additive manufacturing should incorporate shear-related experimental metrics and scaling considerations rather than rely solely on consolidation-based fitting.