Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm

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A review on machine learning in 3D printing: applications, potential, and challenges (2020, methodology): “(Scime and Beuth 2018a, b).”
In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review (2021, methodology): “applied an unsupervised ML algorithm to anomaly detection via camera monitoring [117].”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, background): “Thus, much research in this subject can be found in multiple real industrial scenarios such as automotive [6], manufacturing [7], energy [8], or industrial sensor networks [9].”
Tackling Climate Change with Machine Learning (2019, methodology): “ML is applied to improve those processes, for example through failure detection [184, 185] or material design [186].”
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024, background): “YOLO finds applications in industrial settings for automation and quality control [50].”
Metallurgy, mechanistic models and machine learning in metal printing (2020, methodology): “In other studies, automated image analysis has been used to detect anomalies during the powder spreading of PBF-L to identify defects using machine learning 175 .”