Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material inconsistency and part variability remain, mainly due to its layer-wise fabrication. A key issue is heat accumulation during DED, which affects the material microstructure and properties. While closed-loop control methods for heat management are common in DED research, few integrate real-time monitoring, physics-based modeling, and control in a unified framework. Our work presents a digital twin (DT) framework for real-time predictive control of DED process parameters to meet specific design objectives. We develop a surrogate model using Long Short-Term Memory (LSTM)-based machine learning with Bayesian Inference to predict temperatures in DED parts. This model predicts future temperature states in real time. We also introduce Bayesian Optimization (BO) for Time Series Process Optimization (BOTSPO), based on traditional BO but featuring a unique time series process profile generator with reduced dimensions. BOTSPO dynamically optimizes processes, identifying optimal laser power profiles to attain desired mechanical properties. The established process trajectory guides online optimizations, aiming to enhance performance. This paper outlines the digital twin framework's components, promoting its integration into a comprehensive system for AM.

翻译：激光定向能量沉积（DED）在增材制造（AM）中具有制造复杂几何形状和材料梯度分布的优势。然而，由于其逐层制造的特性，材料不均匀性和零件变异性等挑战依然存在。关键问题在于DED过程中的热量积累会影响材料微观结构和性能。尽管面向热量管理的闭环控制方法在DED研究中较为常见，但很少有研究将实时监测、基于物理的建模与控制集成到统一框架中。本研究提出一种数字孪生（DT）框架，用于实时预测控制DED工艺参数以实现特定设计目标。我们开发了一个基于长短期记忆（LSTM）机器学习与贝叶斯推断的代理模型，用于预测DED零件的温度。该模型可实时预测未来温度状态。同时，基于传统贝叶斯优化（BO），我们提出了面向时间序列工艺优化的贝叶斯优化（BOTSPO）方法，其特色在于具有降维特性的独特时间序列工艺剖面生成器。BOTSPO能够动态优化工艺，识别最优激光功率剖面以获得目标力学性能。所建立的工艺轨迹将引导在线优化，旨在提升性能。本文阐述了该数字孪生框架的组件，推动其集成至增材制造的综合性系统中。