High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated.
This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the $R^2$ metric, which provides a relative measure of the model's performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by $R^2$ and maintains generalizability to geometries that were not included in the training process.
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）
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）
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）
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。
