发表机构
Indian Institute of Science; Visvesvaraya National Institute of Technology (VNIT)（印度科学学院; 维斯瓦纳亚·纳拉辛哈·维尔蒂萨瓦拉国家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对富钼α+β钛合金热变形行为建模难题，开发两个STAR-PINNs模型。通过自动微分施加物理约束，DRX感知型模型有双输出架构及相关正则化等。模型能准确再现多种关系，为钛合金先进工艺建模提供实用方法。
AI 中文摘要
热变形行为的精确本构建模对于先进结构合金的热机械工艺设计至关重要。传统的阿累尼乌斯型和经验模型无法充分捕捉广泛加工条件下应变硬化、动态回复（DRV）和动态再结晶（DRX）的综合影响。本研究开发了两个堆叠残差物理信息神经网络（STAR-PINNs）来模拟富钼α+β钛合金（Ti-6Al-4Mo-1V-0.1Si）的热变形响应。增强型STAR-PINN纳入了热机械本构约束，而DRX感知型STAR-PINN采用双输出架构来考虑再结晶动力学。两个模型都使用了在800至1050摄氏度温度和0.01至10每秒应变率下收集的实验流动应力数据训练的共享残差编码器。通过自动微分实施包括热软化、应变率敏感性、应变硬化和峰值后软化在内的物理信息约束。DRX感知型模型进一步集成了JMAK-阿弗拉米正则化、DRX饱和约束以及基于阿累尼乌斯的具有可调参数的一致性，通过潜在特征融合将预测的DRX分数直接与应力输出联系起来。DRX感知型STAR-PINN实现了RMSE = 11.69 MPa，MAE = 4.83 MPa，R^2 = 0.9850，交叉验证的RMSE为12.47 +/- 0.26 MPa。该模型准确再现了温度依赖的流动曲线、DRX动力学和齐纳-霍洛蒙关系，同时保持了物理上一致的本构行为。这些结果表明，物理信息深度学习为本构建模提供了一个强大且可解释的框架，为钛合金的先进工艺建模提供了一种实用方法。
英文摘要
Accurate constitutive modeling of hot deformation behavior is essential for designing thermomechanical processes in advanced structural alloys. Conventional Arrhenius-type and empirical models do not adequately capture the combined effects of strain hardening, dynamic recovery (DRV), and dynamic recrystallization (DRX) across broad processing conditions. In this study, two Stacked Residual Physics-Informed Neural Networks (STAR-PINNs) were developed to simulate the hot deformation response of a Mo-rich $α+β$ titanium alloy (Ti-6Al-4Mo-1V-0.1Si). The Enhanced STAR-PINN incorporated thermomechanical constitutive constraints, while the DRX-Aware STAR-PINN employed a dual-output architecture to account for recrystallization kinetics. Both models used a shared residual encoder trained on experimental flow stress data collected at temperatures from 800 to 1050 degrees C and strain rates between 0.01 and 10 per second. Physics-informed constraints, including thermal softening, strain-rate sensitivity, strain hardening, and post-peak softening, were enforced through automatic differentiation. The DRX-Aware model further integrated JMAK-Avrami regularization, DRX saturation constraints, and Arrhenius-based consistency with tunable parameters, directly linking the predicted DRX fraction to stress output via latent-feature fusion. The DRX-Aware STAR-PINN achieved RMSE = 11.62 MPa, MAE = 4.73 MPa, R^2 = 0.9852, and a cross-validated RMSE of 12.74 +/- 0.38 MPa. The learned JMAK parameters converged to k = 2.03 and n = 1.99, remaining close to the conventional values while being inferred directly from the data. The model effectively mirrored flow curves influenced by temperature, DRX development, and Zener Hollomon correlations under interpolation scenarios while preserving physically coherent constitutive behavior.
Comments38 pages, 13 figures
DOI：10.1016/j.jallcom.2026.191202
