Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Moon, Junhyung; Yang, Minyeol; Park, Songmi; and Jeong, Jongpil, "From Scarcity to Abundance: Expansion Manufacturing Data through Limited Defect Images" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 5.
https://aisel.aisnet.org/hicss-57/da/manufacturing/5
From Scarcity to Abundance: Expansion Manufacturing Data through Limited Defect Images
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/da/manufacturing/5