发表机构
Virginia Tech; Florida A&M University–Florida State University; DEVCOM, Army Research Laboratory（弗吉尼亚理工大学; 佛罗里达农工大学-佛罗里达州立大学; 美军研发司令部陆军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对搅拌摩擦增沉积中热-流强耦合及热物性随温度变化的问题，提出多任务耦合物理信息神经网络MCoPINN，从稀疏数据重建性质并预测三维速度与温度场，计算成本远低于有限体积法。
AI 中文摘要
搅拌摩擦增材沉积（AFSD）涉及由摩擦热、剧烈塑性变形和工具施加边界条件产生的强耦合热场与材料流场。高保真有限体积法（FVMs）能够精确求解这些耦合场，但其计算成本限制了在不同工艺条件下的重复评估。另一个建模挑战源于热物理性质的强温度依赖性。将热导率、密度和比热视为常数会在预测热-力学响应时引入显著误差。本研究开发了一种稳态多任务耦合物理信息神经网络（MCoPINN），用于预测三维速度场和温度场，同时从稀疏材料数据中重建温度相关的热物理性质。理论分析将MCoPINN的预测误差形式化分解为性质重建和神经场求解器两部分的贡献。首先使用一个受控的一维非线性热传导问题来演示该误差分解，并评估稀疏数据下的性质重建。随后将该框架应用于AFSD，并与FVM基准和实验热电偶测量结果进行对比评估。MCoPINN重现了基准热场和材料流场，同时相对于常数性质的CoPINN改善了热预测。基准FVM每个操作条件约需52小时，而MCoPINN训练约需8.5小时。结果表明，MCoPINN能够在AFSD全场预测中考虑温度相关的热物理性质，同时所需计算量显著低于FVM基准。
英文摘要
Additive friction stir deposition (AFSD) involves strongly coupled thermal and material-flow fields generated by frictional heating, severe plastic deformation, and tool-imposed boundary conditions. High-fidelity finite-volume methods (FVMs) can resolve these coupled fields accurately, but their computational cost limits repeated evaluation across process conditions. A separate modeling challenge arises from the strong temperature dependence of thermophysical properties. Treating thermal conductivity, density, and specific heat as constants can introduce substantial error in the predicted thermo-mechanical response. This work develops a steady-state multi-task coupled physics-informed neural network (MCoPINN) that predicts the three-dimensional velocity and temperature fields while reconstructing temperature-dependent thermophysical properties from sparse material data. A theoretical analysis formally decomposes the MCoPINN prediction error into contributions from property reconstruction and the neural field solver. A controlled one-dimensional nonlinear heat-conduction problem is first used to demonstrate this error decomposition and evaluate property reconstruction under sparse data. The framework is then applied to AFSD and evaluated against an FVM benchmark and experimental thermocouple measurements. MCoPINN reproduces the benchmark thermal and material-flow fields while improving the thermal prediction relative to the constant-property CoPINN. The benchmark FVM required approximately 52 hours per operating condition, whereas MCoPINN required about 8.5 hours of training. The results demonstrate that MCoPINN can account for temperature-dependent thermophysical properties in full-field AFSD prediction while requiring significantly less computation than the FVM benchmark.
Comments14 pages, 12 figures, 7 tables
