【摘要】：目前全球市場上不斷出現各種產品的召回事件,使得人們都越來越關心產品質量,尤其是產品的來源及去向,而企業(yè)同時還注重提高問題產品質量原因分析的能力。根據電子元器件產品的特點,本文研究了質量追溯的關鍵技術,即產品追蹤定位和問題產品模糊診斷技術。產品追蹤主要用于搜索生產過程中所有可能造成質量問題的因子;模糊診斷主要對模糊現象進行定量化診斷,從而得出造成質量問題的原因。但是目前在產品追蹤方面的研究主要集中在批次追蹤,缺少與質量分析之間的聯(lián)系,使得其他重要信息缺失;在質量原因分析方面,定量診斷的精度不夠高。因此質量追溯要同時注重追蹤信息的齊全性和診斷精度的提升。針對電子元器件類產品追蹤定位,分析了一般追蹤算法追蹤的信息不完整并且追蹤信息雜亂無章的問題,提出了本文的產品追蹤算法,包括前向追蹤算法和后向追蹤算法,并明確了兩者之間關系。該算法基于批次和約束,既注重批量生產的產品的特點,也注重產品的生產工藝,并結合具有多元組節(jié)點的產品結構樹,彌補了一般追蹤算法的缺陷,同時可以得到多種退化型樹狀結構,為后續(xù)的質量問題模糊分析建模提供了依據。針對質量問題產品的原因分析,傳統(tǒng)的經驗判斷手段既無法定量化描述可能的原因,也無法提高定性分析的準確度。在分析了其他行業(yè)運用的診斷技術后,以提高診斷精確度為目的,提出了基于模糊診斷的質量原因分析法。首先運用統(tǒng)計法和退化型樹狀結構對產品問題征兆和原因進行建模,提出了德爾菲優(yōu)序數法,并使用了數理統(tǒng)計和德爾菲優(yōu)序數法確定了各征兆和影響因子的隸屬度,隨后確定了模糊關系矩陣,最后采用了模型算子Ⅳ進行診斷,并通過實例進行驗證,結果表明該方法不僅能診斷出一般方法診斷不出的結果,還具有更高的診斷精度。平臺信息化建設是企業(yè)提高管理水平的重要手段�；诋a品追蹤和質量問題原因分析理論上的研究,設計與實現了軟件系統(tǒng),利用其強大的數據管理功能的優(yōu)勢,為本文研究提供數據支撐和進一步的理論驗證。
[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，

本文編號：2413423