Product Quality Control And Optimization Technology Oriented To Design-manufacturing-Serving Full Cycle And Application In Large Air Separation Equipment

This paper focuses on the product quality control process and its key technologies. Product quality control and optimization technologies are proposed in this dissertation oriented to the design-manufacturing-sevice full cycle, which is mainly consisted of the analysis of customer requirements, the optimization extraction method of quality characteristics, the optimization method for solving product scheme design information model, the multi-objective optimization quality control of product development project, the robust optimization of product design parameters in product manufacturing stage, the optimization decision of product quality control scheme in service stage and the reliability prediction optimization of product service quality. Furthermore, the effective applications on the quality control of large air separation equipment proved advancement and validity of the proposed method and technology as well.The main contents of this dissertation are as follows:Chapter1gives the reviews of full-lifecycle-oriented product design technology. The connotation and current research of product quality control are discussed as well. Based on the analyzing the deficiencies in existing method of product quality control, the main ideas and research background of product quality control and optimization oriented to the design-manufacturing-service full cycleare given.Chapter2analyzes the mapping relationship between product function and structure based on requirement satisfaction, and the product scheme solving technique. For the complex issue of the mapping between quality characteristics and structure domain in product scheme design, function domain is introduced as intermediary to guide this mapping. First, the customer requirements are filtered by fuzzy Kano model, then the importance degree of quality characteristics are calculated by analytic network process (ANP) accoding to the influence of customer requirements on quality characteristics and the correlation between quality characteristics. Quality characteristics are decomposed in functions, and functions are decomposed in structures. The similarity of concept and characteristics between product function-structure mapping and constraint satisfaction problem (constraint satisfaction problem, CSP) are analyzed, so the conceptual design problem is mapped to the CSP framework, evolutionary game algorithm was employed to solve the CSP model, and the evaluation function is mapped as utility function. The efficiency and effectiveness of the proposed method is illustrated by the scheme design of turbo-expander product.Chapter3proposes the method of multi-objcetve optimization in product development project base on fuzzy chance constrained programming. The fuzzy chance constrained programming is applied to establish a project completion time optimization model based on critical path, a project time-cost optimization model based on employment of resources and a project time-quality optimization model by quality function deployment, then a time-cost-quality multi-objective optimization model is established based on these models. The discrete particle swarm algorithm is applied to solve the multi-objective optimization model, where the fitness function is constructed based on regret degrees of sub-objectives, which are handled by fuzzy simulation technique. The abovementioned approaches are applied to the case of air aompressor product development project optimization to demonstrate the advantage.Chapter4proposes the method of product quality characteristics robust optimization based quantified constraint satisfaction problem (QCSP). When the product structure parameters fluctuate, the product exporting quality characteristcs will be unstable. The structure parameter variation is expressed as the universal variable in QCSP. The upper and lower bounds of the objective functions and constraint functions are calculated with the effect of the universal variable, and the robustness indicators are set according to designer’s preference. Considering the model characteristics, a modular algorithm consisted of interval analysis and he shuffled frog-leaping algorithm is applied to solve the QCSP model, and the Pareto optimal solutions which satisfy the robustness indicators is gained, the optimal solution is selected by the information entropy theory-based approach. The efficiency and effectiveness of the proposed method are illustrated by the product quality characteristics robust optimization design of turbine compressor diffuser.Chapter5proposes the method of product quality control shceme optimization decision based on hybrid model of advanced fuzzy DEMATEL-VIKOR algorithm. For the deficiencies of product quality control shceme evaluation methods, the uncertain information is expressed with intuitionistic fuzzy number. Splitting matrix method is applied in DEMATEL method to maintain the fuzzy characteristic for total-influence matrix. The relationship between evaluation criteria was analyzed using the improved DEMATEL method, which not only revealed causal relationship between criteria, but also carried out the causal classification, importance ranking and weights assignment. For VIKOR method, there is some modification on definition of positive-ideal solution and negtive-solution. The improved VIKOR method helps decision makers to achieve an acceptable compromise solution of a maximum "group utility" of the "majority" and a minimum of the individual regret of the "opponent". Finally, the scheme evaluation of air separation equipment is analyzed as an instance.Chapter6proposes the method of interval prediction for product reliability criterion of service quality based on nonlinear regression. First, prediction neural network of reliability parameters is constructed, which is trained by ELM algorithm. Then, nonlinear regression model is used to construct prediction interval for reliability parameters based on its point value derived from the trained neural network and the weights of network. The immune algorithm is adopted to automate the neural network model selection and adjustment of the weight decay regularizing factor. Model selection and parameter adjustment are carried out through minimization of the prediction interval-based cost function called CPLC, which combines the coverage probability and the mean interval proportional length of PI. Finally, the proposed theory and method is applied to predict the reliability parameter MTBF of air separation equipment, which proved the feasibility and effectiveness of the method in engineering application.Chapter7develops the air separation equipment quality control and optimization system (HY-ASEQCOS) with practical project..The background of system application, implementation scheme, the technical implementation and main functions of the system is elaborated.Chapter8summarizes the key research contents and achievements, and gives conclusions along with recommendations for future research.