From Scarcity to Abundance: Expansion Manufacturing Data through Limited Defect Images
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2024-01-03
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1027
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Abstract
The increasing adoption of IoT sensors, communication capabilities, and software applications in manufacturing environments has led to a growing demand for handling diverse large-scale manufacturing data. This trend indicates that AI is being researched and developed as an essential tool for improving cost-effectiveness and efficiency. Recently, there has been a significant increase in demand for process improvement using deep learning technology in smart manufacturing processes. However, obtaining a sufficient amount of training data in real industrial environments is challenging due to security and cost concerns for companies. Therefore, we propose utilizing generative artificial intelligence to efficiently expand manufacturing datasets. For data augmentation, we use a model that combines Stable Diffusion and LoRA fine tuning, and apply the text generation approach of BLIP. We anticipate that these data augmentation will help to improve the performance of artificial intelligence in the manufacturing field while reducing the cost of data collection.
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Data-driven Services and Servitization in Manufacturing: Innovation, Engineering, Transformation, and Management, data augmentation, data scarcity, generative ai, smart manufacturing
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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