A cloud integrated strategy for reconfigurable manufacturing systems. (December 2020)

• Record Type: Journal Article
• Authors: Guo, Bo; Lee, Fu-Shin; Lin, Chen-I; Lu, Yun-Qing
• Is Part Of: Concurrent engineering, research and applications. Volume 28:Number 4(2020:Dec.)
• Page Start: 305
• Page End: 318
• Publication Date: 2020-12
• Subjects: reconfigurable manufacturing system -- image recognition -- cloud server -- CNC -- robot -- control panel -- invariant feature extraction; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X20958937
• Languages: English
• ISSNs: 1063-293X

Abstract

Manufacturing industries nowadays need to reconfigure their production lines promptly as to acclimate to rapid changing markets. Meanwhile, exercising system reconfigurations also needs to manage innumerous types of manufacturing apparatus involved. Nevertheless, traditional incompatible manufacturing systems delivered by exclusive vendors usually increase manufacture costs and prolong development time. This paper presents a novel RMS framework, which is intended to implement a Redis master/slave server mechanism to integrate various CNC manufacturing apparatus, hardware control means, and data exchange protocols through developed configurating codes. In the RMS framework each manufacturing apparatus or accessory stands for an object, and information of recognized CNC control panel image features, associated apparatus tuned parameters, communication formats, operation procedures, and control APIs, are stored into the Redis master cloud server database. Through implementation of machine vision techniques to acquire CNC controller panel images, the system effectively identifies instantaneous CNC machining states and response messages once the embedded image features are recognized. Upon demanding system reconfigurations for the manufacturing resources, the system issues commands from Redis local client servers to retrieve the stored information in the Redis master cloud servers, in which the resources for registered CNC machines, robots, and built-in accessories are maintained securely. The system then exploits the collected information locally to reconfigure involved manufacturing resources and starts manufacturing immediately, and thus is capable to promptly response to fast revised orders in a comitative market. In a prototyped RMS architecture, the proposed approach takes advantage of recognized feedback visual information, which is obtained using an invariant image feature extraction algorithm, and effectively commands an industrial robot to accomplish demanded actions on a CNC control panel, as a regular operator does daily in front of the CNC machine for manufacturing.