Deep Learning Based Synthetic Image Generation for Defect Detection in Additive Manufacturing Industrial Environments

Matuszczyk, Daniel; Tschorn, Niklas; Weichert, Frank

doi:10.1109/icmerr56497.2022.10097812

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Wafer bin maps (WBMs), a visual summary of quality products in the semiconductor industry, are generated using a deep convolutional generative adversarial network (DCGAN) to deal with the imbalanced data problem and increase the accuracy of each defect pattern classification 37 . Fused Deposition Modeling (FDM) manufactured parts in additive manufacturing use physically based rendering (PBR) working together with GAN to generate defective images 38 . The cycleGAN 39 promotes the realistic appearance of a rendered image by learning transformation from real‐world photography FDM images.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent IoT framework with GAN‐synthesized images for enhanced defect detection in manufacturing

Aramkul,

Sugunnasil

2023

Computational Intelligence

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The manufacturing industry is always exploring techniques to optimize processes, increase product quality, and more accurately identify defects. The technique of deep learning is the strategy that will be used to handle the issues presented. However, the challenge of using AI in this domain is the small and imbalanced dataset for training affected by the severe shortage of defective data. Moreover, data acquisition requires significant labor, time, and resources. In response to these demands, this research presents an intelligent internet of things (IoT) framework enriched by generative adversarial network (GAN). This framework was developed in response to the needs outlined above. The framework applies the IoT for real‐time data collection and communication, while GAN are utilized to synthesize high‐fidelity images of manufacturing defects. The quality of the GAN‐synthesized image is quantified by the average FID score of 8.312 for non‐defective images and 7.459 for defective images. As evidenced by the similarity between the distributions of synthetic and real images, the proposed generative model can generate visually authentic and high‐fidelity images. As demonstrated by the results of the defect detection experiments, the accuracy can be enhanced to the maximum of 96.5% by integrating the GAN‐synthesized images with the real images. Concurrently, this integration reduces the occurrence of false alarms.

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Section: Literature Reviewmentioning

confidence: 99%