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.

利用数字孪生框架在实时预测控制激光直接能量熔化 (DED) 过程参数以满足特定设计目标，通过 LSTM 机器学习建立代理模型预测 DED 零件的温度，在此基础上引入贝叶斯优化 (Bayesian Optimization) 进行时间序列过程优化 (Time Series Process Optimization)，确定理想的激光功率轮廓以达到所需的力学性能。

面向增材制造的数字孪生框架：时间序列工艺优化的机器学习与贝叶斯优化

Towards a Digital Twin Framework in Additive Manufacturing: Machine  Learning and Bayesian Optimization for Time Series Process Optimization

To meet the fairly high safety and reliability requirements in practice, the
state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a
close relationship with the degradation performance, has been extensively
studied with the widespread applications of various electronics. The
conventional SOH estimation approaches with digital twin are end-of-cycle
estimation that require the completion of a full charge/discharge cycle to
observe the maximum available capacity. However, under dynamic operating
conditions with partially discharged data, it is impossible to sense accurate
real-time SOH estimation for LIBs. To bridge this research gap, we put forward
a digital twin framework to gain the capability of sensing the battery's SOH on
the fly, updating the physical battery model. The proposed digital twin
solution consists of three core components to enable real-time SOH estimation
without requiring a complete discharge. First, to handle the variable training
cycling data, the energy discrepancy-aware cycling synchronization is proposed
to align cycling data with guaranteeing the same data structure. Second, to
explore the temporal importance of different training sampling times, a
time-attention SOH estimation model is developed with data encoding to capture
the degradation behavior over cycles, excluding adverse influences of
unimportant samples. Finally, for online implementation, a similarity
analysis-based data reconstruction has been put forward to provide real-time
SOH estimation without requiring a full discharge cycle. Through a series of
results conducted on a widely used benchmark, the proposed method yields the
real-time SOH estimation with errors less than 1% for most sampling times in
ongoing cycles.

通过数字孪生框架对锂离子电池进行实时健康状况估计，实现了不需要完成完整放电循环的实时 SOH 估计，并提出了能够处理可变训练数据、探索不同训练时间重要性以及在线实现的解决方案。