A proactive material handling method for CPS enabled shop-floor. (February 2020)

• Record Type: Journal Article
• Authors: Wang, Wenbo; Zhang, Yingfeng; Zhong, Ray Y.
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 61(2020)
• Publication Date: 2020-02
• Subjects: Cyber physical system (CPS) -- Material handling -- Shop-floor -- Prediction model -- Remaining processing time -- Large-size product; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2019.101849
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: Overall architecture of a novel proactive material handling method was proposed. Remaining processing time of work-in-progresses was predicted dynamically. Predicted move tasks can be assigned according to future status of smart trolleys. The efficiency and effectiveness are illustrated by a case study. Abstract: Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.