Affiliation:
1. National Kaohsiung University of Science and Technology
Abstract
The paper proposed a deep convolutional neural network together with image processing techniques to detect assembly defects of vehicle components in assembly lines. Traditional detection method such as automatic optical inspection is strongly affected by environmental variation coming from the changes of light source, transfer belt, and component type, therefore, complicated thresholds should be adjusted case by case. The proposed method tries to avoid these problems which is fast and straight forward with satisfactory detection accuracy compared to traditional method.
Publisher
Trans Tech Publications, Ltd.
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science
Reference8 articles.
1. J.H. Ye and Q.C. Hsu: Sensors Mater. Vol. 30 (2018), pp.2637-2652.
2. H.M. Ahmad and A.Rahimi: J. Manuf. Syst. Vol. 64 (2022), pp.181-196.
3. M. Haselmann, D.P. Gruber, and P. Tabatabai: 17th IEEE ICMLA (2018), pp.1237-1242.
4. F.P. Basamakis, A.C. Bavelos, D. Dimosthenopoulos, A. Papavasileiou, and S. Makrisa: Procedia CIRP. Vol. 115 (2022), pp.166-171.
5. I. Konovalenko, P. Maruschak, J. Brezinová, J. Viňáš and Jakub Brezina: Metals. Vol. 10 (2020) 846.