English(EN) Physics-based prediction, uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes

基于物理的模型预测3D打印中的IN718织构

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了一种新颖的两阶段基于物理的模型，用于预测激光粉末床熔合过程中Inconel 718 (IN718) 的晶体织构强度。该模型首先将工艺变量映射到熔化模式，然后通过整合经验物理模型和随机森林残差模型来预测织构。这种方法包含了衰减对支持不佳数据校正的机制，并抑制了物理有效范围之外的预测，与黑盒模型相比，展示了更好的可迁移性和可靠性。 AI

影响通过将物理学与机器学习相结合，增强了材料科学的可预测性，从而在增材制造中实现更好的质量控制。

排序理由该集群包含一篇详细介绍材料科学应用新物理模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Yisheng Lu, John Riris, Jie Song, Yao Fu, Jie Chen · 2026-09-17 04:00

LPBF散焦模式下IN718晶体织构强度的基于物理的预测、不确定性量化与决策

arXiv:2609.18863v1 Announce Type: new Abstract: Reliable prediction of crystallographic texture in laser powder bed fusion is critical for linking process conditions with anisotropic response and for qualification. However, black-box models may fail under shift and cannot disting…

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LPBF散焦模式下IN718晶体织构强度的基于物理的预测、不确定性量化与决策

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