Research On Problem-driven Quality Improvement For Manufacturing Enterprise

In China, repetitive quality incidents continue to occur in the manufacturing process of our basic parts, which often forced companies to be engaged in event handling passively. Not only are caused huge economic losses, but also product quality can not be effectively improved. China's manufacturing enterprises are urgent to resolve these difficulties. From the grants of the National Natural Science Foundation and the weapons and equipment pre-research Fund, this paper researched on the technologies for quality improvement of issue-driven manufacturing companies.In chapter one, the background of research, surveys the current research situation of quality improvement home and abroad is introduced and summarizes the development trends and hot spots of quality improvement, which leads to this dissertation research contents. Based on "issues", the research content includes quality improvement theory and key techniques for manufacturing companies.In chapter two, the perplexities in quality control field of our manufacturing companies is analyzed and puts forward the requirement for the management of classification for the quality problems features. Based on the comparative study of current management of features classification and orthogonal classification, the dissertation proposes the QP-DOC model, which lay the foundation for the issue-driven quality improvement..In chapter three, based on the model for features classification management, the dissertation proposes a issue-driven quality improvement model and discusses description model of business process based on ExGQM, structured function module plan of quality improvement mode and knowledge resource management. This lays the foundation for the implement of issue-driven quality improvement.In chapter four, the method of knowledge integration for quality improvement case based on knowledge map is disscussed. It includes formal description of knowledge map system, identification and description of knowledge nodes, knowledge navigator and knowledge node retrieval vector construction.In chapter five, the decision-making method of production process monitoring points based on error propagation between quality indexes is put forward. And the studies of researches on production process monitoring.In chapter six, the strengths and bottleneck of Bayesian network in quality issues diagnosis and the deconstruction method of Bayesian networks in process quality problem diagnosis oriented is analyzed. It emphasizes on the decomposition of Bayesian network model for diagnosis, model base construction, and sub-net model reconfiguration based on Bayesian network. It tries to provide a draw on ideas and methods for the construction of Bayesian network model for process quality problem diagnosis.Based on the researches of the front, chapter seven studies the application of issues-driven quality improvement in manufacturing enterprises with the bearing company as target.In chapter eight, the contents and innovations of the dissertation is summarized and lookedto the future research.