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In conclusion, our research aimed to advance Anomaly Detection in the Additive Manufacturing process, especially under the complexities of time-varying conditions. We have successfully constructed a diverse set of anomaly images using computer graphics, which were further validated by experts in DLP 3D printing.
We explored various anomaly detection methods, including Variational Autoencoders and CNN-LSTMs, to tackle the challenge. However, it was our proposed CNN model that stood out, achieving a substantial improvement in performance with an accuracy of 97.45%, as demonstrated in our experimental results.
The image below highlights the efficacy of our method, where an anomaly detected by our system is circled in red. This validates our approach and offers a promising direction for future research in ensuring the quality and reliability of 3D printed products. |
IEEE/ICACT20240043 Slide.15
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