A flexible configuration method of distributed manufacturing resources in the context of social manufacturing

Under the influence of Industry 4.0, manufacturing resources (MRs) across regions and enterprises realize ubiquitous interconnection and real-time data acquisition via communication network and internet of things, which makes it possible for distributed MRs to provide appropriate production services according to changeable market demand. Therefore, social manufacturing (Social Mfg) system came into being. Under the Social Mfg environment, a flexible configuration method of distributed MRs is put forward in this paper, which is qualified to quickly respond to order demand and production disturbance. Firstly, superior manufacturing services are selected from the resource pool of the platform according to the types of subtasks. Secondly, high-quality service compositions capable of completing a complicated manufacturing task are obtained by NSGA-III algorithm, which meets the requirements of multi-objective optimization in terms of time, cost and quality. In addition, a MRs collaborative network model is constructed based on the historical cooperation data. The Louvain algorithm is employed to detect the manufacturing communities (MCs) from the collaborative network. Finally, the MCs are applied to provide the optimal service schemes for personalized demand at a fast speed, as well as realizing dynamic reconfiguration of MRs under abnormal disturbance. Experimental results demonstrate that the proposed method possesses significant performance in both efficiency and robustness, and makes great contributions to the development of Social Mfg in terms of resource configuration. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Under the influence of Industry 4.0, a flexible configuration method for manufacturing resources in the Social Mfg environment is proposed in this study. By selecting superior manufacturing services, obtaining high-quality service compositions with algorithms, and constructing manufacturing communities through collaborative networks, the method can quickly provide optimal service schemes for personalized demand.