A cost-effective manufacturing process recognition approach based on deep transfer learning for CPS enabled shop-floor. (August 2021)

• Record Type: Journal Article
• Authors: Liu, Bufan; Zhang, Yingfeng; Lv, Jingxiang; Majeed, Arfan; Chen, Chun-Hsien; Zhang, Dang
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 70(2021)
• Publication Date: 2021-08
• Subjects: Manufacturing process recognition -- Transfer learning (TL) -- Cyber-physical systems (CPS) -- Convolutional neural network (CNN) -- Deep learning (DL); Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2021.102128
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: A cost-effective manufacturing process recognition approach is developed based on deep transfer learning. The cost-effectiveness reflects in better accuracy with fewer training samples and less training time. The performance of the proposed approach is compared with different methods. The feasibility and effectiveness of the proposed approach are proved on two different datasets. Abstract: The rapid development of the industrial Internet of Things has promoted manufacturing to develop towards the cyber-physical system, of which highly accurate process recognition plays an important role in achieving proactive monitoring of intelligent manufacturing process. Compared to the traditional handcrafted feature-based method, deep model owns convenience in terms of extracting feature automatically for the recognition. However, training a deep model is time-consuming and also requires large-scale training samples. To solve these problems and obtain high accuracy in the meanwhile, a deep transfer learning-based manufacturing process recognition approach is proposed in this study. A pre-trained model based on a convolutional neural network is used to extract low dimensional features followed by a fine-tuning process to target the specific process recognition task. Experimental verification of two datasets was conducted to demonstrate this cost-effective method. The results showed the proposed method can get better accuracy with less training time and fewer training samples.