基于图神经网络的增材制造现场异常检测（英文版）

英文标题：In-situ Anomaly Detection in Additive Manufacturing with Graph Neural Networks中文摘要：本研究使用激光输入信息训练模型，预测名义激光熔化条件并计算异常得分以检测制造缺陷，显示异常检测方法是开发可靠的缺陷检测方法的重要工具。英文摘要：Transforming a design into a high-quality product is a challenge in metaladditive manufacturing due to rare events which can cause defects to form.Detecting these events in-situ could, however, reduce inspection costs, enablecorrective action, and is the first step towards a future of tailored materialproperties. In this study a model is