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.

通过利用光电二极管传感器数据的机器学习框架，本研究集中于检测过热异常，其中提取了光电二极管数据的三组特征（MSMM、MSQ 和 MSD）并采用多数投票集成方法，实现了层次化的过热异常检测，结果表明，所涉及的方法在检测层次化过热异常方面取得了卓越的结果，显着优于现有文献中使用相同基准数据集的方法。