ESAFORM Benchmark 2025: predicting stainless steel PBF-LB part density using statistical, data-driven, and physics-informed machine learning models derived from process parameters and in-situ monitoring data

Abstract This study benchmarks multiple data-driven methodologies for predicting relative density (RD) of 316 L stainless steel fabricated via Powder Bed Fusion–Laser Beam (PBF-LB), as part of the ESAFORM Benchmark 2025 AMDmodel initiative. Two datasets (DS-01 and DS-02), each with 256 specimens from a 4-factor, 4-level design of experiments, were produced on different PBF-LB systems equipped with equivalent in-situ infrared (IR) melt-pool pyrometry. Failed builds (RD = 60%) were retained to allow models to learn from both nominal and catastrophic processing conditions, a scenario rarely addressed in PBF-LB machine learning (ML). Statistical analysis of variance (ANOVA) confirmed that conventional process parameters alone are weak predictors (R² ≈ 0.49). In contrast, sensor-driven supervised ML models using melt-pool thermal descriptors performed substantially better. Recursive feature elimination highlighted the interquartile range and mode of thermal signatures as dominant predictors; an XGBoost model using only these achieved R² = 0.93 on DS-01. Hybrid models combining parameters and IR descriptors performed slightly worse (R² = 0.92), indicating mild redundancy. Cross-system transferability was limited: ML models trained on DS-01 underperformed on DS-02 due to IR input-domain divergence despite RD distributions between both domain sources showing high inter-laboratory consistency. To address this, a physics-informed ML framework (PIML) using symbolic regression (QLattice) embedded dimensionless physical priors. Resulting compact expressions dominated by normalized laser power and volumetric energy density achieved R² = 0.83–0.93 under cross-system validation. Overall, sensor-driven ML models are effective for machine-specific monitoring and layer-wise closed-loop control, whereas PIML provide system-agnostic process parameter-window estimation for design-stage optimization.