Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. Hence, the proposed method offers a machine learning (ML) framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies. In doing so, three sets of features are extracted from the raw photodiode data: MSMM (mean, standard deviation, median, maximum), MSQ (mean, standard deviation, quartiles), and MSD (mean, standard deviation, deciles). These three datasets are used to train several ML classifiers. Cost-sensitive learning is used to handle the class imbalance between the "anomalous" layers (affected by overheating) and "nominal" layers in the benchmark dataset. To boost detection accuracy, our proposed ML framework involves utilizing the majority voting ensemble (MVE) approach. The proposed method is demonstrated using a case study including an open benchmark dataset of photodiode measurements from an LPBF specimen with deliberate overheating anomalies at some layers. The results from the case study demonstrate that the MSD features yield the best performance for all classifiers, and the MVE classifier (with a mean F1-score of 0.8654) surpasses the individual ML classifiers. Moreover, our machine learning methodology achieves superior results (9.66% improvement in mean F1-score) in detecting layer-wise overheating anomalies, surpassing the existing methods in the literature that use the same benchmark dataset.

翻译：过热异常检测对于激光粉末床熔融（LPBF）增材制造（AM）零件的质量和可靠性至关重要。本研究聚焦于利用光电二极管传感器数据检测过热异常。光电二极管传感器能够从熔池中采集高频数据，反映工艺动态和热历史。因此，所提出的方法提供了一个机器学习（ML）框架，利用光电二极管传感器数据实现逐层过热异常检测。为此，从原始光电二极管数据中提取了三组特征：MSMM（均值、标准差、中位数、最大值）、MSQ（均值、标准差、四分位数）和MSD（均值、标准差、十分位数）。这三组数据集被用于训练多个机器学习分类器。采用代价敏感学习来处理基准数据集中“异常层”（受过热影响）与“名义层”之间的类别不平衡问题。为提高检测精度，我们提出的机器学习框架利用了多数投票集成（MVE）方法。通过一个案例研究来验证所提方法，该案例包含来自LPBF试样的光电二极管测量公开基准数据集，其中部分层存在人为引入的过热异常。案例研究结果表明，MSD特征在所有分类器中表现最佳，且MVE分类器（平均F1分数为0.8654）优于单个机器学习分类器。此外，我们的机器学习方法在逐层过热异常检测中取得了更优结果（平均F1分数提升9.66%），超越了文献中使用相同基准数据集的现有方法。