The global smart manufacturing market is growing rapidly as developed countries (Germany, USA, Japan) as well as late comers such as China are now recognizing the importance of “smart manufacturing” and promoting active policies to foster related ecosystems. Policies to revitalize manufacturing through the convergence of cutting-edge ICT and manufacturing technology are already in progress. Some such policies include Industry 4.0, the Advanced Manufacturing Partnership of the United States, Japan’s Industrial Revitalization Plan, and China’s Made in China 2025. As a result of the gradual shift from product standardization to product customization, the importance of machine vision in manufacturing has also been increasing. However, it is difficult to develop a standard machine vision method because there are different specifications for meeting the individual demands of different manufacturing industries. Moreover, it is difficult to apply such a standard machine vision method to artificial intelligence because defective data for learning in the manufacturing industry are not frequently generated and stored. Therefore, it is conducted manually or visually inspected by workers. This study applies the primary feature of matching models for a label printing process to efficiently detect defects with high performance and applies a deep learning model to maximize performance. Our proposed method achieved an accuracy of 97% with a feature matching model and 99.8% accuracy with the deep learning model.
|Number of pages||8|
|Journal||Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry|
|Publication status||Published - 2021|
Bibliographical notePublisher Copyright:
© 2021 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Media Technology