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<p>Only a limited number of alloys can be additively manufactured industrially today, to form defect-free parts. Optimization of process parameters of laser power, scan speed, powder layer thickness, and hatch spacing during laser-based additive manufacturing, particularly laser powder bed fusion (LPBF), is of prime importance to take advantage of unique microstructures and enhanced mechanical properties of these 3D-printed parts. This study presents a physics-informed, data-driven framework for predicting defect formation and constructing printability maps in LPBF of industrial alloys. By integrating solidification-coupled computational fluid dynamics (CFD) simulations, single-track experiments, and machine learning classification, the approach captures the effect of melt-pool flow characteristics and material thermophysical properties on defect formation. The resulting printability maps accurately delineate stable, balling, lack of fusion, and keyhole regimes across SS316L, IN718, and AlSi10Mg alloys. SHapley Additive exPlanation (SHAP)-based model interpretation reveals that process energy input and alloy properties jointly govern defect transitions, aligning with established LPBF physics. The framework offers a generalizable route for identifying defect-free processing windows in new alloys, reducing empirical optimization efforts and accelerating qualification for additive manufacturing applications.</p>

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Physics-Informed Data-Driven Determination of Printability Maps of Industrial Alloys for Laser Powder Bed Fusion Process with Understandings from Numerical Modeling

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Gurrala Laxmi Prasanna,
  • Sushil Patel,
  • Pikee Priya

摘要

Only a limited number of alloys can be additively manufactured industrially today, to form defect-free parts. Optimization of process parameters of laser power, scan speed, powder layer thickness, and hatch spacing during laser-based additive manufacturing, particularly laser powder bed fusion (LPBF), is of prime importance to take advantage of unique microstructures and enhanced mechanical properties of these 3D-printed parts. This study presents a physics-informed, data-driven framework for predicting defect formation and constructing printability maps in LPBF of industrial alloys. By integrating solidification-coupled computational fluid dynamics (CFD) simulations, single-track experiments, and machine learning classification, the approach captures the effect of melt-pool flow characteristics and material thermophysical properties on defect formation. The resulting printability maps accurately delineate stable, balling, lack of fusion, and keyhole regimes across SS316L, IN718, and AlSi10Mg alloys. SHapley Additive exPlanation (SHAP)-based model interpretation reveals that process energy input and alloy properties jointly govern defect transitions, aligning with established LPBF physics. The framework offers a generalizable route for identifying defect-free processing windows in new alloys, reducing empirical optimization efforts and accelerating qualification for additive manufacturing applications.