Transforming a design into a high-quality product is a challenge in metal additive manufacturing due to rare events which can cause defects to form. Detecting these events in-situ could, however, reduce inspection costs, enable corrective action, and is the first step towards a future of tailored material properties. In this study a model is trained on laser input information to predict nominal laser melting conditions. An anomaly score is then calculated by taking the difference between the predictions and new observations. The model is evaluated on a dataset with known defects achieving an F1 score of 0.821. This study shows that anomaly detection methods are an important tool in developing robust defect detection methods.

翻译：将设计转化为高质量产品是金属增材制造中的一项挑战，这是由于罕见事件可能导致缺陷形成。然而，原位检测这些事件可以降低检测成本，实现纠正措施，并成为迈向未来定制材料特性的第一步。本研究训练了一个基于激光输入信息的模型，用于预测标称激光熔化条件。然后通过计算预测值与新观测值之间的差异来得出异常分数。该模型在已知缺陷的数据集上进行了评估，F1得分为0.821。本研究表明，异常检测方法是开发稳健缺陷检测方法的重要工具。