Demand-based manufacturing service capability estimation of a manufacturing system in a social manufacturing environment

Abstract

The exponential growth of cross-enterprise collaboration in recent years has facilitated the emergency of a new manufacturing mode called social manufacturing, wherein order winning hinges mostly on their customer and demand-oriented manufacturing service capability rather than the traditional factors such as relationships and distances. Therefore, a manufacturing service capability estimation model is proposed in this article, which comprises two sub-models, that is, a machining service capability (M-capability) model and a production service capability (P-capability) model. The former explains what kinds of manufacturing service a manufacturing system can provide, while the latter estimates how much manufacturing service the manufacturing system can produce in a certain time period. Besides, each of the two sub-models is considered from both single-socialized manufacturing resources and multi-socialized manufacturing resources’ manufacturing system perspectives. First, an ontology and Semantic Web–based model is established for estimating the M-capability of a single-socialized manufacturing resource manufacturing system and further extended to multiple socialized manufacturing resources manufacturing systems. Then, P-capability evaluation model based on rough set–based propagation neural network is proposed for a single-socialized manufacturing resource manufacturing system. Afterward, the results of rough set–based propagation neural network are further utilized as the inputs of the latter particle swarm optimization–based P-capability estimation model for multiple socialized manufacturing resources manufacturing systems. Finally, a typical case is fully studied to illustrate the feasibility of the proposed models.