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.
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.
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.
Aiming at the quality early warning problem of manufacturing process, this study develops a quality early warning system base on Web form design, combining some auto parts companies operating practice. In addition, it analyses the design principle and application methods of the system. It has been proved that the system achieves real-time monitoring, diagnosis, early warning to abnormal volatility, and prevents the defective products from occurrence and outflow, and gives corresponding diagnosis views to abnormal fluctuations according to historical data. It has been proved that this system is effective in practice.
Optoelectronic industry is one of the pillar cornerstone industries in the 21st century. For China optoelectronic enterprises, how to participate in the global competition by means of the world-class quality has become the survival or perish subject. Based on the analysis of optoelectronic products and its quality management characteristics, this paper proposed the product lifecycle quality management model which is customer demands-driven and six sigma process control targeted by emphasizing the process control based on fact and data. This paper suggested and illustrated the prototype system of optoelectronic product lifecycle quality management combined with the actual quality management for demonstrating the feasibility of model.
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