Research On Technologies And Application Of Data Mining For PLM

The modern manufacturing enterprises are faced with pressures on responding to customer's demand rapidly, shortening product delivery time, improving product quality and services, because of economic era with global economic integration, personal demand and high-speed of product delivery. In this pressure, data analysis and mining for product lifecycle management (PLM) are needed for enterprises, to meet the needs of various departments and leaders for decision-makings, which makes the enterprise to achieve changes on "Made in China" to "Created in China", and promotes transition to manufacturing services enterprises by responding to the changes of internal and external environment rapidly. A research on technologies and application of data mining for product lifecycle is provided in this dissertation, based on the national 863 project and the important technical project. The main content is as follows:In chapter 1, the background of research is introduced. The demand of data mining in manufacturing enterprise especially for PLM (Product Lifecycle Management) is advanced. The current problems in data mining applications in manufacturing enterprise are summed up. The basic concepts, related theory and research status of data mining are introduced, and the application of data mining in product lifecycle stages is summarized. The content and structure of the thesis are given, and the significance of the research paper is discussed.In chapter 2, the four-layer system frame of the data mining for PLM is proposed, including the data layer, method layer, the results layer and application layer. The key technology of data mining for PLM is discussed, including classification, coding, ordering and integration technologies. The data, methods, results and applications of data mining and their various classification models, coding model and integrated model are expounded, which is needed in the data mining process for PLM.In chapter 3, a variety of mining models are divided into general models and specific models for the demand of PLM. A data mining model libraries is constructed using object-oriented method and "model dictionary+model program libraries" form. The model is managed and maintained uniformly by Model Base Management System.In chapter 4, a data mining process for PLM is designed. The appropriate data mining models and data sources can be found quickly through the matching degree of the attributes describe of data and method models with data mining needs. A data mining system for PLM is researched and developed starting from the data mining process for PLM. The function processes of the system are described. The technologies and tools which used by the system and the relevant function modules of system are analyzed, which manages the data mining process, demands, models and results.In chapter 5, the data mining application of product lifecycle is researched. First, a various needs in application, data processing and related mining method models in the different stages are described and the related mining results are obtained. Such as the analysis of patents in the design phase, the production quality factors in the manufacturing stage, customer segmentation and sales forecast in the sales service stage, etc. And then the data mining applications based on quality data integration for PLM are studied. Finally, the application of data mining results integration for PLM is studied. It includes the integration of data mining results for the demand of various departments of product lifecycle and business leaders.In chapter 6, all achievements and innovations of this dissertation are summarized and the future research direction is put forward.