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http://dx.doi.org/10.14775/ksmpe.2019.18.12.038

Development of Checker-Switch Error Detection System using CNN Algorithm  

Suh, Sang-Won (Dual Mechanics Co., Ltd.)
Ko, Yo-Han (Department of Electronic Engineering, Chonbuk National University)
Yoo, Sung-Goo (Saemangeum Eenterprise Development Agency, Kunsan National University)
Chong, Kil-To (Advance Electronics & Information Research Center, Chonbuk National University)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.18, no.12, 2019 , pp. 38-44 More about this Journal
Abstract
Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.
Keywords
Error Detection; Machine Learning; CNN, Checker Switch;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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