文章基本信息

标题：Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing
本地全文：下载
作者：Sang-Hyun Park ; Kang-Hee Lee ; Ji-Su Park 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：5
页码：2697
DOI：10.3390/su14052697
语种：English
出版社：MDPI, Open Access Journal
摘要：In manufacturing a product, product defects occur at several stages. This study makes the case that one can build a smart factory by introducing it into the manufacturing process of small-scale scarce products, which mainly solves the defect problem through visual inspection. By introducing an intelligent manufacturing process, defects can be minimized, and human costs can be lowered to enable sustainable growth. In this paper, in order to easily detect defects occurring in the manufacturing process, we studied a deep learning-based automatic defect detection model that can train product characteristics and determine defects using open sources. To verify the performance of this model, it was applied to the disposable gas lighter manufacturing process to detect the liquefied gas volume defect of the lighter, and it was confirmed that the detection accuracy and processing time were sufficient to apply to the manufacturing process.