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<p>The geometry of the melt-pool in laser powder bed fusion (LPBF) strongly influences the plausibility of defect formation and subsequently the mechanical properties of the printed specimen. Hence, an optimal combination of process parameters is critical to additively manufacture defect-limited parts. Here, we employ predictive LPBF models to identify this optimal process parameter landscape for Inconel 718. By systematically analyzing the melt-pool morphology under varied processing conditions, we identify a process window that significantly enhances dimensional accuracy and reduces deformation by 46% compared to standard practices. Our findings demonstrate that a prudent use energy input is critical for achieving defect-limited, high-integrity builds, offering practical guidelines for improved additive manufacturing of metallic components.</p>

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Process parameter optimization for predictive melt-pool morphology in laser powder bed fusion for Inconel 718

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Debajyoti Adak,
  • Somnath Roy,
  • Ganesh Balasubramanian

摘要

The geometry of the melt-pool in laser powder bed fusion (LPBF) strongly influences the plausibility of defect formation and subsequently the mechanical properties of the printed specimen. Hence, an optimal combination of process parameters is critical to additively manufacture defect-limited parts. Here, we employ predictive LPBF models to identify this optimal process parameter landscape for Inconel 718. By systematically analyzing the melt-pool morphology under varied processing conditions, we identify a process window that significantly enhances dimensional accuracy and reduces deformation by 46% compared to standard practices. Our findings demonstrate that a prudent use energy input is critical for achieving defect-limited, high-integrity builds, offering practical guidelines for improved additive manufacturing of metallic components.