汽车生产质量控制管理应用研究

发布时间：2018-04-06 21:02

本文选题：汽车　切入点：质量控制系统　出处：《沈阳建筑大学》2016年硕士论文

【摘要】：随着汽车制造产业的高速发展,产品生产制造过程是质量控制管理的重要环节,同时也是产品质量问题的主要来源之一。对此本文采用一种更加有效的质量控制系统来进一步提高汽车生产制造的质量控制管理水平。首先利用现代化的数据采集方法和制造执行系统MES的思想,结合质量AUDIT管理,使质量数据的有效性有了大大的提高,为质量控制管理打下坚实的基础,然后通过大量、及时、准确的质量检测数据作保证,决策者就可以全面掌握企业的质量状态。本文在对质量检测数据的分析时,研究了目前的AP聚类算法和最简规则提取算法,并分别在谱分析和概念格的基础上提出以下两种优化算法。针对传统的质量控制管理的质量检测数据规模大、属性多等复杂因素,提出了一种基于谱分析的AP聚类优化算法(Affinity Propagation based on Spectrum analyze,AP-SA)。首先,通过采用谱分析技术将分布在高维非线性的数据点集映射到几乎线性的子空间上,映射过程实现高维数据降至低维。最后,通过AP聚类算法对映射在低维空间上的数据进行聚类,从而提高了AP算法在高维空间上的聚类性能,。仿真实验结果表明,该优化算法相比于传统AP算法,在低维数据中无明显的优势,但随着实验的数据集的样本规模与维数的增加,在高维数据中的该方法降低了聚类时间的同时,也保证了较好的聚类效果。针对传统的质量控制管理存在过程统计复杂、生产质量决策规则数量大且提取复杂等问题,提出了一种基于概念格的最简规则提取优化算法。该优化算法利用扩展不可分辨矩阵和概念格之间的关系构造概念格模型,使质量检测同生产紧密结合。在挖掘质量决策规则时,通过构造概念格时的概念结点之间的偏序关系,直接判断决策属性集合中的所有概念结点有无父结点,再根据父结点的内涵得到最简规则集。不但给企业提供直观的、易理解的最简规则集,提高了产品质量控制管理水平,还简化了最简规则提取步骤。仿真实验结果表明,该优化算法具有一定稳定性的同时,也提高了提取的效率。最后,应用一个工程实例验证了本课题设计的可行性与有效性。
[Abstract]:With the rapid development of automobile manufacturing industry, product manufacturing process is an important part of quality control and management, and also one of the main sources of product quality problems.In this paper, a more effective quality control system is adopted to further improve the quality control management level of automobile production and manufacture.Firstly, by using the modern data acquisition method and the idea of manufacturing execution system (MES), combining with the quality AUDIT management, the validity of quality data has been greatly improved, which lays a solid foundation for quality control management, and then through a large number of, timely,With accurate quality data, the decision-maker can master the quality state of the enterprise.In this paper, the current AP clustering algorithm and the minimum rule extraction algorithm are studied in the analysis of quality detection data, and the following two optimization algorithms are proposed based on spectral analysis and concept lattice, respectively.In view of the complex factors such as large scale and many attributes of traditional quality control management, an AP clustering optimization algorithm based on spectrum analysis is proposed, which is Affinity Propagation based on Spectrum analyze AP-SAA.Firstly, the high dimensional data set is mapped to almost linear subspace by spectral analysis technique, and the high dimensional data is reduced to low dimension.Finally, the AP clustering algorithm is used to cluster the data mapped on the low-dimensional space, which improves the clustering performance of the AP algorithm in the high-dimensional space.The simulation results show that compared with the traditional AP algorithm, the proposed algorithm has no obvious advantages in the low-dimensional data, but with the increase of the sample size and dimension of the experimental data set,The method in high dimensional data reduces the clustering time and ensures better clustering effect.Aiming at the problems of complex process statistics, large quantity of production quality decision rules and complex extraction in traditional quality control management, an optimization algorithm based on concept lattice for extracting the simplest rules is proposed.Based on the relationship between extended indiscernibility matrix and concept lattice, this optimization algorithm constructs concept lattice model, which makes quality detection closely combined with production.In mining quality decision rules, by constructing the partial order relation between concept nodes in concept lattice, we can directly judge whether all concept nodes in the decision attribute set have parent nodes, and then get the simplest rule set according to the connotation of parent node.It not only provides enterprises with intuitionistic and easy to understand the simplest rule set, but also simplifies the process of extracting the simplest rules by improving the level of product quality control and management.The simulation results show that the algorithm is stable and the efficiency of extraction is improved.Finally, an engineering example is used to verify the feasibility and effectiveness of the design.
【学位授予单位】：沈阳建筑大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U468

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