Anomaly detection in additive manufacturing processes using supervised classification with imbalanced sensor data based on generative adversarial network

Document Type

Article

Publication Date

6-1-2024

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 a manufacturing process, the number of sensor data samples collected from a normal state is usually much more than that from an abnormal state. This issue causes imbalanced training data for classification analysis, 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.

Identifier

85162727849 (Scopus)

Publication Title

Journal of Intelligent Manufacturing

External Full Text Location

https://doi.org/10.1007/s10845-023-02163-8

e-ISSN

15728145

ISSN

09565515

First Page

2387

Last Page

2406

Issue

5

Volume

35

Grant

N00014-18-1-2794

Fund Ref

Office of Naval Research

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