The cloud manufacturing brings forward a new idea of manufacturing resource sharing with service-oriented. Recent advances in information technology, such as cloud computing, internet of things, make it easier for heterogeneous resources in different regions to remote collaboration in cloud manufacturing. To help improve the success of distributed manufacturing resource sharing for service provider and user in building material and equipment enterprise (BMEE), the order-oriented cloud service library (OCSL) and order-based model for shared manufacturing resources (OMSMR) are proposed after analysing the management features of manufacturing resources in BMEE. The OCSL gives a relationship description between task orders and related services. Moreover, a case study is undertaken to evaluate the proposed model. The model brings into manufacturing industry for manufacturing resource sharing with a number of benefits such as openness, integrity and traceability.

In many manufacturing processes, the abnormal changes of some key process parameters could result in various categories of faulty products. In this paper, a machine learning approach is developed for dynamic quality prediction of the manufacturing processes. In the proposed model, an extreme learning machine is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. The proposed model is successfully applied to a japanning-line, which improves the product quality and saves manufacturing cost.

Order to discrete manufacturing enterprises typical business process as background, analyzes its in the order lifecycle process. Extract the key nodes information in the business process, business process model, data chain model is established. Finally, the use of middleware technology for the actual situation of discrete manufacturing enterprises, established the order lifecycle execution process architecture model. Has important significance for discrete manufacturing enterprise order lifecycle information management system.

The production mode of equipment manufacturing enterprise is large single-piece production driven by project for customer personalization. This is complex in a large project, where having so many involved resources, persons and content can become more difficult to manage. Whereas, the current project management system mainly focuses on some single functions and ignores the requirements of project lifecycle for process tracking management. Therefore, on the foundation of investigation and analysis, the models of material flow and cash flow are proposed to monitor the implementation process of project and developed a lifecycle-oriented Project Management System (PMS) by using ASP.NET platform and SQL SERVER database technology in equipment manufacturing enterprise. Finally, the application effect of PMS software in equipment manufacturing enterprise demonstrates its feasibility and effectiveness.

Aiming at the diversity and heterogeneity of manufacturing resources in cloud manufacturing, the multi-hierarchy relevance model (MHRM) based on category and environmental level was proposed. Moreover, the OWL was used to describe the cloud manufacturing resources. Finally, cloud manufacturing resource ontology instance was built by Protégé.