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<p>Despite significant progress in additive manufacturing, processing defects remain a persistent challenge. Artificial intelligence (AI) enabled early defect detection and process optimization is promising solution for this problem. In this study, real image data from a composite 3D printing setup was used to evaluate the anomaly detection performance of three models: Autoencoder, Support Vector Machine (SVM), and the Zero Bias Deep Neural Network (DNN). The results demonstrate that the Zero bias model achieved an accuracy of 97.96%, significantly outperforming the Autoencoder (93.38%) and SVM (89.80%). Multiple thresholds in the zero bias model enable explain ability.</p>

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Evaluating AI Algorithms for Identifying Anomalies in Composite Additive Manufacturing

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Deepak Kumar,
  • Yongxin Liu,
  • Sirish Namilae

摘要

Despite significant progress in additive manufacturing, processing defects remain a persistent challenge. Artificial intelligence (AI) enabled early defect detection and process optimization is promising solution for this problem. In this study, real image data from a composite 3D printing setup was used to evaluate the anomaly detection performance of three models: Autoencoder, Support Vector Machine (SVM), and the Zero Bias Deep Neural Network (DNN). The results demonstrate that the Zero bias model achieved an accuracy of 97.96%, significantly outperforming the Autoencoder (93.38%) and SVM (89.80%). Multiple thresholds in the zero bias model enable explain ability.