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<p>The paper at hand evaluates the validity of the transient heat equation with phase change and temperature-dependent coefficients as a model to predict the evolution of melt pools for rapid turnaround scan strategies in a laser powder bed fusion (PBF-LB) additive manufacturing (AM) process. In such scenarios, residual heat effects due to short laser-off times between tracks significantly impact the melt pool morphology. Results show that the conduction model accurately captures the rapidly evolving melt pool morphologies under these conditions. This is a fundamental discovery, as the ability to predict such dynamic scenarios has previously only been attributed to much more involved models which include fluid-like effects that otherwise require computational fluid dynamics (CFD). However, the presented validation (e.g., validated comparison to surface topography measurements) clearly demonstrates that a large portion of the observed effects is actually captured by the heat diffusion equation, including a common model of phase change. As a proof of concept for an application of this insight, feedforward control is investigated. Therein, the validated model predicts the melt pool morphology for a modified scan strategy with an increased pause time between the tracks that reduces melt pool distortions observed in the original configuration.</p>

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The trace of heat: on the predictive power of modeling transient diffusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月26日

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  • Vijaya Holla,
  • Jesse Redford,
  • Philipp Kopp,
  • Stefan Kollmannsberger

摘要

The paper at hand evaluates the validity of the transient heat equation with phase change and temperature-dependent coefficients as a model to predict the evolution of melt pools for rapid turnaround scan strategies in a laser powder bed fusion (PBF-LB) additive manufacturing (AM) process. In such scenarios, residual heat effects due to short laser-off times between tracks significantly impact the melt pool morphology. Results show that the conduction model accurately captures the rapidly evolving melt pool morphologies under these conditions. This is a fundamental discovery, as the ability to predict such dynamic scenarios has previously only been attributed to much more involved models which include fluid-like effects that otherwise require computational fluid dynamics (CFD). However, the presented validation (e.g., validated comparison to surface topography measurements) clearly demonstrates that a large portion of the observed effects is actually captured by the heat diffusion equation, including a common model of phase change. As a proof of concept for an application of this insight, feedforward control is investigated. Therein, the validated model predicts the melt pool morphology for a modified scan strategy with an increased pause time between the tracks that reduces melt pool distortions observed in the original configuration.