Imbalanced Data Classification via Generative Adversarial Network with Application to Anomaly Detection in Additive Manufacturing Process

• URL: http://arxiv.org/abs/2210.17274v1
• Date: Fri, 28 Oct 2022 16:08:21 GMT
• Title: Imbalanced Data Classification via Generative Adversarial Network with Application to Anomaly Detection in Additive Manufacturing Process
• Authors: Jihoon Chung, Bo Shen, and Zhenyu (James) Kong
• Abstract summary: This paper proposes a novel data augmentation method based on a generative adversarial network (GAN) using additive manufacturing process image sensor data. The diverse and high-quality generated samples provide balanced training data to the classifier. The effectiveness of the proposed method is validated by both open-source data and real-world case studies in polymer and metal AM processes.
• Score: 5.225026952905702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defects) detection. However, since abnormal states (with defects) occur much less frequently than normal ones (without defects) in the manufacturing process, the number of sensor data samples collected from a normal state outweighs that from an abnormal state. This issue causes imbalanced training data for classification models, thus deteriorating the performance of detecting abnormal states in the process. It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set. To achieve this goal, this paper proposes a novel data augmentation method based on a generative adversarial network (GAN) using additive manufacturing process image sensor data. The novelty of our approach is that a standard GAN and classifier are jointly optimized with techniques to stabilize the learning process of standard GAN. The diverse and high-quality generated samples provide balanced training data to the classifier. The iterative optimization between GAN and classifier provides the high-performance classifier. The effectiveness of the proposed method is validated by both open-source data and real-world case studies in polymer and metal AM processes.

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