发表机构
Virginia Tech; Macromolecules Innovation Institute, Virginia Tech（弗吉尼亚理工学院; 弗吉尼亚理工学院大分子创新研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出两阶段物理模型预测IN718激光粉末床熔融织构，结合经验模型与随机森林残差，通过k近邻衰减和离焦准则实现可靠迁移与弃权（不执行），优于黑盒模型。
AI 中文摘要
在激光粉末床熔融中，晶体织构的可靠预测对于将工艺条件与各向异性响应联系起来以及进行认证至关重要。然而，黑盒模型在分布偏移下可能失效，且无法区分数据支持不足与物理有效性丧失。本研究开发了一个针对Inconel 718中<001> || BD（构建方向）织构的两阶段基于物理的模型。第一阶段将工艺变量映射到熔化模式和熔池几何形状。第二阶段通过结合经验物理模型与随机森林残差模型来预测织构。k近邻权重衰减了支持不足查询的残差修正，而一项针对特定研究的面积束功率密度准则在采用的传导包络之外拒绝预测。在保留的物理有效集上评估保形区间，并使用SHAP和Sobol分析评估残差敏感性。在受控的留一离焦交叉验证下，物理锚定模型实现了R²=0.778，而黑盒模型为-0.001，门控混合模型为0.750。在留一组交叉验证下，门控混合模型达到R²=0.592，而黑盒模型为0.538。在分组交叉验证下，保留集的覆盖率为92.9%，平均全宽为3.65倍均匀分布（MUD）；在转移到保留的+80毫米离焦区间时，覆盖率为100%，宽度为3.21 MUD。一个示例性映射产生了127-187 GPa的保留BD弹性模量跨度。在来自单独构建样本集的九个条件下，框架拒绝了三个，衰减了三个，并匹配了其余条件的测量排序。将数据适用性、物理有效性和预测不确定性分离为不同的决策，使框架能够在无约束模型无法迁移的情况下进行迁移，并在任何模型类别都无法充分表现时拒绝预测。
英文摘要
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 distinguish weak data support from loss of physical validity. This study develops a two-stage physics-based model for <001> || BD (build direction) texture in Inconel 718. Stage 1 maps process variables to melting mode and melt pool geometry. Stage 2 predicts texture by combining an empirical physics model with a random-forest residual model. A k-nearest-neighbor weight attenuates residual corrections for poorly supported queries, while a study-specific areal beam-power-density criterion withholds predictions outside the adopted conduction envelope. Conformal intervals are evaluated on the retained physics-valid set, and SHAP and Sobol analyses assess residual sensitivity. Under a controlled leave-one-defocus-out evaluation, the physics anchor achieved R^2 = 0.778, against -0.001 for the black-box model and 0.750 for the gated hybrid. Under leave-one-group-out cross-validation, the gated hybrid reached R^2 = 0.592 against 0.538 for the black-box model. Retained-set coverage was 92.9% at a mean full width of 3.65 multiples of a uniform distribution (MUD) under grouped cross-validation and 100% at a width of 3.21 MUD under transfer to a withheld +80 mm defocus regime. An illustrative mapping produced a retained BD elastic-modulus span of 127-187 GPa. On nine conditions from a separately built sample set, the framework withheld three, attenuated three, and matched the measured ordering for the rest. Separating data applicability, physics validity, and predictive uncertainty into distinct decisions lets the framework transfer where an unconstrained model does not, and withhold predictions where no model class performs adequately.
Comments69 pages, 9 figures, 9 tables. Includes supplementary material (S1-S7)
