Process optimization for metal additive manufacturing (AM) is crucial to ensure repeatability, control microstructure, and minimize defects. Despite efforts to address this via the traditional design of experiments and statistical process mapping, there is limited insight on an on-the-fly optimization framework that can be integrated into a metal AM system. Additionally, most of these methods, being data-intensive, cannot be supported by a metal AM alloy or system due to budget restrictions. To tackle this issue, the article introduces a Reinforcement Learning (RL) methodology transformed into an optimization problem in the realm of metal AM. An off-policy RL framework based on Q-learning is proposed to find optimal laser power ($P$) - scan velocity ($v$) combinations with the objective of maintaining steady-state melt pool depth. For this, an experimentally validated Eagar-Tsai formulation is used to emulate the Laser-Directed Energy Deposition environment, where the laser operates as the agent across the $P-v$ space such that it maximizes rewards for a melt pool depth closer to the optimum. The culmination of the training process yields a Q-table where the state ($P,v$) with the highest Q-value corresponds to the optimized process parameter. The resultant melt pool depths and the mapping of Q-values to the $P-v$ space show congruence with experimental observations. The framework, therefore, provides a model-free approach to learning without any prior.
Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学）电气工程系）；Department of Mechanical Engineering, Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学）机械工程系）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
RWTH Aachen University（亚琛工业大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）
Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）新加坡制造技术研究院（SIMTech））；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）先进再制造与技术中心（ARTC））；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）
针对混合制造场景下传统CAD特征自动识别方法无法识别增材特征、尺寸提取不准的问题，提出基于Python Open Cascade构建增减材复合特征合成数据集，用HGCNN实现高精度特征识别与尺寸提取，助力CAD/CAPP/CAM集成与工艺规划优化。
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network
Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing
Trinity College Dublin（都柏林圣三一学院）；Maynooth University（梅努斯大学）；Dublin City University（都柏林城市大学）；I-Form Advanced Manufacturing Research Centre（I-Form先进制造研究中心）
An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing
College of Engineering and Computer Science, University of Texas Rio Grande Valley（工程与计算机科学学院，德克萨斯理工大学Rio Grande Valley分校）；School of Industrial Engineering and Management, Oklahoma State University（工业工程与管理学院，俄克拉荷马州立大学）
