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What is Anomaly Detection in Additive Manufacturing about?
This paper presents an unsupervised deep learning approach using an encoder-decoder architecture for detecting anomalies in sequential sensor data from additive manufacturing processes. The method is demonstrated on a real-world dataset, successfully identifying injected anomalies and providing insights into temperature variations during manufacturing. The proposed algorithm aims to enhance process control and improve part quality in selective laser sintering applications.
- Author
- joseph22kuruvilla
- Language
- EN