A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifecycle. For Laser Directed Energy Deposition of Wire (DED-LB/w) additive manufacturing processes, digital twins may help to control the residual stress design in build parts. This study focuses on providing faster-than-real-time and highly accurate surrogate models for the formation of residual stresses by employing neural ordinary differential equations. The approach enables accurate prediction of temperatures and altered structural properties like stress tensor components. The developed surrogates can ultimately facilitate on-the-fly re-optimization of the ongoing manufacturing process to achieve desired structural outcomes. Consequently, this building block contributes significantly to realizing digital twins and the first-time-right paradigm in additive manufacturing.

翻译：数字孪生是一种精确复现物理实体的虚拟表征，可在全过程生命周期内实现双向实时数据交换。对于线材激光定向能量沉积增材制造工艺，数字孪生有助于控制构件中的残余应力设计。本研究重点通过采用神经常微分方程，为残余应力的形成提供超实时且高精度的代理模型。该方法能够准确预测温度及应力张量分量等结构属性的演变。所开发的代理模型最终可实现制造过程的在线实时重优化，以获得预期的结构性能。因此，该基础模块为实现增材制造领域的数字孪生与"一次成型"范式作出了重要贡献。