AI CNN Based Defect Inspection System of a Governor Using a Rotating Platform

被引：0

作者：

Kim, Dong Hun ^{[1
]}

Jeong, Jong Min ^{[2
]}

机构：

[1] Department of Electronics Engineering, Kyungnam University

[2] Department of Mechatronics Engineering, Kyungnam University

来源：

Journal of Institute of Control, Robotics and Systems | 2024年 / 30卷 / 10期

关键词：

convolutional neural networks (CNN); defect detection; engine governor; inception; xception;

D O I：

10.5302/J.ICROS.2024.24.0162

中图分类号：

学科分类号：

摘要：

In this study, an automated defect-detection system based on a convolutional neural network (CNN) for rapid governor rotation was developed and its performance was evaluated. In the proposed study, the governor was used in diesel engines to regulate the fuel injection amounts to regularly maintain the engine's rotational speed. The governor was rotated using a servomotor at speeds of 700, 1000, 2000, 2800, and 3200 rpm, and the degree of the governor’s opening was captured using a camera. CNN was employed to detect the defect states using captured images of the governor's opening degree. The experimental results compare and evaluate the performance using two CNN algorithms: Xception and Inception. This study demonstrates the application of CNN-based models to improve defect-detection systems in manufacturing processes. Accordingly, CNN algorithms extract and learn features from images, rendering them useful for the detection of various types of defects. © ICROS 2024.

引用

页码：1082 / 1089

页数：7

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