Theories With Its Application Of Collaborative Intelligent And Multi-Scale Quality Control For Key Part Machines In Large Scale Air Separation Equipment

To deal with multi-scale, collaboration, coupling, uncertainty et.al problems in the quality control process of air separation equipment, a new multi-scale collaborative intelligent technology consisting of customer requirements, mappings between customer requirements and quality characteristics, quality characteristics fulfillments level, suppliers chain construction, module division and granule decision, quality control for each scale, quality control scheme evaluation and selection, is proposed in this dissertation. Relying on important projects supported by national basic foundation, referring to realistic application of corporation, the effeteness and validity is proved.The main contents of this dissertation are as follows:Chapter1gives the reviews of quality control methods for common product, which consists of development process and current research trend. The physical structure and quality control characteristics of air separation equipment are discussed, as well. Based on the deficiencies analyzed in existing method of quality control, the main ideas and research background of quality control for air separation equipment products are given. At last, a new multi-scale collaborative intelligent technology of quality control is given.Chapter2analyzes issues existing in most traditional methods for mapping from customer requirements to quality characteristics, a novel method using a combination of multi-criteria decision models and multi-objective optimization algorithm is put forward. With the focus on influences among customer requirements, fuzzy DEMATEL method is used for determining importance ratings of customer requirements. Shafer integral is applied to aggregate relationships between requirements and quality characteristics, so importance ratings of quality characteristics will be determined. Considering other factors conditioning quality characteristics implement, the multiple objective optimization models for quality characteristics fulfillment levels is constructed. The improved strength pareto evolutionary algorithm(NSGA-Ⅱ) was employed to acquire the Pareto solutions set. Finally, entropy optimal selection technique was adopted to choose the best solution from the Pareto solutions set. Finally, the feasibility and validity of the proposed method was illustrated.Chapter3aims at two-dimensional limitations about components optimization and suppliers evaluation existing in the traditional optimization design methods where suppliers were involved with, a novel two-stage method consisting of multi-objective optimization and dynamic multi-attribute decision was proposed. In the first stage, multi-objective optimization model for quality, cost and delivery time was built, and Epsilon-strength Pareto evolutionary algorithm was employed to solve abovementioned model and to get finite Pareto solutions. In the second stage, Bernoulli forecasting-based fuzzy dynamic multi-attribute decision was applied to evaluate suppliers pertaining to Pareto solutions, and then the optimal supplier for each component was selected and the best product planning scheme was acquired. A case study of product development of large scale deep cooling air-separating equipment was provided to illustrate the application and validation of the proposed method through simulation and computation.Chapter4analyzes some prevalent limitations of current product module partition, along with the features of product configuration, Fuzzy C Means, which is criticized because of clustering objective function invalidity and climb-hill method for searching solutions, is improved to plan product modular architecture. The improved Fuzzy C Means Cluster is joined with a novel algorithm, named Shuffled Frog-Leaping Algorithm, so the intelligent cluster algorithm is a combination of those two and used for product granularity division. On the base of analysis for main factors affecting configuration design, three quantified indices, namely, satisfaction degree, assembly complexity and variant design complexity, are proposed to evaluate validity for granularity division results and to decide optimal module components. Finally, a practical case is studied, so feasibility and validity can be proven.Chapter5analyzes quality and cost control process for air separation equipment, which were affected by lots of uncertain factors hard to model precisely. To that end, set pair analysis(SPA) was adopted to build the multi-objective relative degree of nearness optimization model for quality and cost control. Evolutionary cellular learning automata algorithm was adaptively improved to solve that optimization model, and then quality and cost control alternatives set's priority ordering, basic ordering, was acquired under the relative certain condition. Considering uncertain influences, fuzzy set-valued statistics was utilized to obtain discrepancy degree coefficient Δ. So that the basic ordering was reordered in the light of relation coefficient, and the optimal product quality and cost control concept alternative was selected. At the end of this paper, large scale deep cooling air-separating equipment's quality and cost control process as a practical case was provided to illustrate the application and validation of the proposed method through simulation and computation.Chapter6points out that air separation equipment concept evaluation is a classic multiple criteria group collaborative decision-making process with lots of uncertain factors involved. For one thing, a-cut fuzzy evaluation method was applied to evaluate each criterion of product design concept. Taking interrelations and non-linear combination between those evaluation criteria into account, a criteria aggregation model based on Choquet integral was introduced for product design concept holistic evaluation. And then, preference comparison matrices were constructed and algorithm for priority of fuzzy complementary judgment matrix was adopted, so that priority ordering for was acquired. On the other hand, aiming at cross-function experts' evaluation opinions with discrepancy, the improved evidence theory was utilized to synthesize each expert decision result of product design concepts preference sorting, and the relatively stable and harmonious decision-making result for product design concept evaluation was reached. At the end, a practical case was provided to illustrate the application and validation of the proposed method through simulation and computation.Chapter7relies national research projects and develops the air separation equipment quality control digital information system (HY-ASEQCS). Through vivid demonstration of utilization with the graph and scripts, the validity and feasibility of the new technology and method proposed in this paper is shown.Chapter8summarizes the key research contents, novelties, and achievements, and givens conclusions along with recommendations for future research.