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

International Journal of Material Forming, vol. 19

Abstract

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.

Authors 20

1. Medad C.C. Monu corresponding

   I-Form Advanced Manufacturing Research Centre · Dublin City University

   Affiliation as printed

   DCU Institute for Advanced Processing Technology, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

   I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

2. Éanna McCarthy

   I-Form Advanced Manufacturing Research Centre · Dublin City University

   Affiliation as printed

   DCU Institute for Advanced Processing Technology, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

   I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

3. Abhilash Puthanveettil Madathil

   University of Strathclyde

   Affiliation as printed

   National Manufacturing Institute Scotland, University of Strathclyde, Paisley, Renfrew, UK

4. Josiah Cherian Chekotu

   I-Form Advanced Manufacturing Research Centre · Dublin City University

   Affiliation as printed

   DCU Institute for Advanced Processing Technology, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

   I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

5. Irina Ilic

   ETH Zurich

   Affiliation as printed

   Advanced Manufacturing Laboratory, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

6. Merve Nur Doğu

   I-Form Advanced Manufacturing Research Centre · Dublin City University

   Affiliation as printed

   DCU Institute for Advanced Processing Technology, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

   I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

7. Cian Hughes

   I-Form Advanced Manufacturing Research Centre · Dublin City University

   Affiliation as printed

   DCU Institute for Advanced Processing Technology, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

   I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

8. Rongfei Juan

   Aalto University

   Affiliation as printed

   Materials to Products, Department of Energy and Mechanical Engineering, Aalto University, Espoo, Finland

9. Junhe Lian Aachen Institute of Metal Forming

   RWTH Aachen University · Aalto University

   Affiliation as printed

   Institute of Metal Forming, RWTH Aachen University, Aachen, Germany

   Materials to Products, Department of Energy and Mechanical Engineering, Aalto University, Espoo, Finland

10. Constantinos Vassiades

    University of Strathclyde

    Affiliation as printed

    National Manufacturing Institute Scotland, University of Strathclyde, Paisley, Renfrew, UK

11. Olga Bylya

    University of Strathclyde

    Affiliation as printed

    National Manufacturing Institute Scotland, University of Strathclyde, Paisley, Renfrew, UK

12. Kim Darosa

    Centre National de la Recherche Scientifique · Université de Bordeaux · Institut de Mécanique et d'Ingénierie de Bordeaux

    Affiliation as printed

    University of Bordeaux, Arts et Métiers, CNRS, I2M UMR, Talence, France

13. Robin Kromer

    Centre National de la Recherche Scientifique · Université de Bordeaux · Institut de Mécanique et d'Ingénierie de Bordeaux

    Affiliation as printed

    University of Bordeaux, Arts et Métiers, CNRS, I2M UMR, Talence, France

14. Abdul Herrim Seidou

    University of Liège

    Affiliation as printed

    Aerospace and Mechanics Department, MMS, University of Liège, Liège, Belgium

15. Sankhya Mohanty

    Technical University of Denmark

    Affiliation as printed

    Department of Civil and Mechanical Engineering, Technical University of Denmark, Lyngby, Denmark

16. Anne Marie Habraken

    University of Liège

    Affiliation as printed

    ArGEnCo dpt, MSM team, University of Liège, Liège, Belgium

17. Anne Mertens

    University of Liège

    Affiliation as printed

    Aerospace and Mechanics Department, MMS, University of Liège, Liège, Belgium

18. Otto Laitinen

    ETH Zurich

    Affiliation as printed

    Advanced Manufacturing Laboratory, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

19. Michael R. Tucker

    ETH Zurich

    Affiliation as printed

    Advanced Manufacturing Laboratory, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

20. Dermot Brabazon

    I-Form Advanced Manufacturing Research Centre · Dublin City University

    Affiliation as printed

    DCU Institute for Advanced Processing Technology, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

    I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland