面向电子元器件产品的质量追溯系统设计与实现
发布时间：2019-01-22 17:47
【摘要】：目前全球市场上不断出现各种产品的召回事件,使得人们都越来越关心产品质量,尤其是产品的来源及去向,而企业同时还注重提高问题产品质量原因分析的能力。根据电子元器件产品的特点,本文研究了质量追溯的关键技术,即产品追踪定位和问题产品模糊诊断技术。产品追踪主要用于搜索生产过程中所有可能造成质量问题的因子;模糊诊断主要对模糊现象进行定量化诊断,从而得出造成质量问题的原因。但是目前在产品追踪方面的研究主要集中在批次追踪,缺少与质量分析之间的联系,使得其他重要信息缺失;在质量原因分析方面,定量诊断的精度不够高。因此质量追溯要同时注重追踪信息的齐全性和诊断精度的提升。针对电子元器件类产品追踪定位,分析了一般追踪算法追踪的信息不完整并且追踪信息杂乱无章的问题,提出了本文的产品追踪算法,包括前向追踪算法和后向追踪算法,并明确了两者之间关系。该算法基于批次和约束,既注重批量生产的产品的特点,也注重产品的生产工艺,并结合具有多元组节点的产品结构树,弥补了一般追踪算法的缺陷,同时可以得到多种退化型树状结构,为后续的质量问题模糊分析建模提供了依据。针对质量问题产品的原因分析,传统的经验判断手段既无法定量化描述可能的原因,也无法提高定性分析的准确度。在分析了其他行业运用的诊断技术后,以提高诊断精确度为目的,提出了基于模糊诊断的质量原因分析法。首先运用统计法和退化型树状结构对产品问题征兆和原因进行建模,提出了德尔菲优序数法,并使用了数理统计和德尔菲优序数法确定了各征兆和影响因子的隶属度,随后确定了模糊关系矩阵,最后采用了模型算子Ⅳ进行诊断,并通过实例进行验证,结果表明该方法不仅能诊断出一般方法诊断不出的结果,还具有更高的诊断精度。平台信息化建设是企业提高管理水平的重要手段。基于产品追踪和质量问题原因分析理论上的研究,设计与实现了软件系统,利用其强大的数据管理功能的优势,为本文研究提供数据支撑和进一步的理论验证。
[Abstract]:At present, there are a variety of product recalls in the global market, which makes people pay more and more attention to the quality of products, especially the origin and destination of products, while the enterprises also pay attention to improving the ability of cause analysis of the quality of the problem products. According to the characteristics of electronic component products, this paper studies the key technologies of quality traceability, that is, product tracking and positioning and fuzzy diagnosis of problematic products. Product tracking is mainly used to search all the factors that may cause quality problems in the production process, and fuzzy diagnosis is mainly used to quantitatively diagnose fuzzy phenomena, so as to obtain the causes of quality problems. However, the current research on product tracking mainly focuses on batch tracking, which is lack of connection with quality analysis, which makes other important information missing, and the accuracy of quantitative diagnosis is not high enough in quality cause analysis. Therefore, quality traceability should pay attention to the completeness of tracking information and the improvement of diagnostic accuracy at the same time. Aiming at the tracking location of electronic components, this paper analyzes the problem that the tracking information of the general tracking algorithm is incomplete and the tracking information is chaotic, and puts forward the product tracking algorithm in this paper, including the forward tracking algorithm and the backward tracking algorithm. The relationship between the two is clarified. Based on batch and constraint, the algorithm not only pays attention to the characteristics of batch production products, but also pays attention to the production process of products, and combines the product structure tree with multi-group nodes to make up for the defects of the general tracking algorithm. At the same time, a variety of degenerate tree structures can be obtained, which provides the basis for the subsequent fuzzy analysis modeling of quality problems. In view of the cause analysis of quality problems, the traditional means of empirical judgment can neither quantitatively describe the possible causes nor improve the accuracy of qualitative analysis. After analyzing the diagnostic techniques used in other industries, a quality cause analysis method based on fuzzy diagnosis is proposed in order to improve the diagnostic accuracy. Firstly, the statistical method and the degenerate tree structure are used to model the symptoms and causes of the product problem, and the Delphi superior ordinal number method is proposed, and the membership degree of each symptom and influence factor is determined by using mathematical statistics and Delphi optimal ordinal number method. Then the fuzzy relation matrix is determined and the model operator 鈪，
本文编号：2413423
[Abstract]:At present, there are a variety of product recalls in the global market, which makes people pay more and more attention to the quality of products, especially the origin and destination of products, while the enterprises also pay attention to improving the ability of cause analysis of the quality of the problem products. According to the characteristics of electronic component products, this paper studies the key technologies of quality traceability, that is, product tracking and positioning and fuzzy diagnosis of problematic products. Product tracking is mainly used to search all the factors that may cause quality problems in the production process, and fuzzy diagnosis is mainly used to quantitatively diagnose fuzzy phenomena, so as to obtain the causes of quality problems. However, the current research on product tracking mainly focuses on batch tracking, which is lack of connection with quality analysis, which makes other important information missing, and the accuracy of quantitative diagnosis is not high enough in quality cause analysis. Therefore, quality traceability should pay attention to the completeness of tracking information and the improvement of diagnostic accuracy at the same time. Aiming at the tracking location of electronic components, this paper analyzes the problem that the tracking information of the general tracking algorithm is incomplete and the tracking information is chaotic, and puts forward the product tracking algorithm in this paper, including the forward tracking algorithm and the backward tracking algorithm. The relationship between the two is clarified. Based on batch and constraint, the algorithm not only pays attention to the characteristics of batch production products, but also pays attention to the production process of products, and combines the product structure tree with multi-group nodes to make up for the defects of the general tracking algorithm. At the same time, a variety of degenerate tree structures can be obtained, which provides the basis for the subsequent fuzzy analysis modeling of quality problems. In view of the cause analysis of quality problems, the traditional means of empirical judgment can neither quantitatively describe the possible causes nor improve the accuracy of qualitative analysis. After analyzing the diagnostic techniques used in other industries, a quality cause analysis method based on fuzzy diagnosis is proposed in order to improve the diagnostic accuracy. Firstly, the statistical method and the degenerate tree structure are used to model the symptoms and causes of the product problem, and the Delphi superior ordinal number method is proposed, and the membership degree of each symptom and influence factor is determined by using mathematical statistics and Delphi optimal ordinal number method. Then the fuzzy relation matrix is determined and the model operator 鈪，
本文编号：2413423
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