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Trustworthy Artificial Intelligence for Real-Time Quality Assurance in Additive Manufacturing
Additive manufacturing requires quality assurance methods capable of detecting process anomalies before defects propagate across subsequent layers. This thesis develops a trustworthy artificial intelligence framework for real-time surface anomaly detection in Fused Deposition Modeling (FDM) using la
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