Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis
Mario Vozza,
Joseph Polden,
Giulio Mattera
et al.
Abstract:Anomaly detection is an important feature in modern additive manufacturing (AM) systems to ensure quality of the produced components. Although this topic is well discussed in the literature, current methods rely on black-box approaches, limiting our understanding of why anomalies occur, making complex the root cause identification and the consequent decision support about the action to take to mitigate them. This work addresses these limitations by proposing a structured workflow designed to enhance the explai… Show more
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