本文選題：決策樹　切入點(diǎn)：聚類分析　出處：《山東大學(xué)》2014年碩士論文

【摘要】：產(chǎn)品質(zhì)量是對(duì)顧客需求的具體反映,也是顧客滿意的必要因素。為了能在競(jìng)爭(zhēng)激烈的市場(chǎng)當(dāng)中占據(jù)有利地位,提高企業(yè)競(jìng)爭(zhēng)力,現(xiàn)代企業(yè)必須加強(qiáng)質(zhì)量管理。隨著科學(xué)技術(shù)的不斷發(fā)展與市場(chǎng)的日趨成熟,市場(chǎng)對(duì)質(zhì)量的要求不斷提高,現(xiàn)有質(zhì)量管理技術(shù)與工具已不能滿足需求。 制造執(zhí)行系統(tǒng)(Manufacturing Execution System,MES)環(huán)境下生產(chǎn)過程中的質(zhì)量數(shù)據(jù)是由車間作業(yè)現(xiàn)場(chǎng)控制收集,系統(tǒng)與工程師進(jìn)行分析處理并儲(chǔ)存在指定的數(shù)據(jù)庫或數(shù)據(jù)倉(cāng)庫中。其數(shù)據(jù)內(nèi)容包含加工記錄、實(shí)時(shí)監(jiān)控信息等,這些數(shù)據(jù)的累積導(dǎo)致分析人員對(duì)數(shù)據(jù)的處理能力下降,其潛在價(jià)值沒有被進(jìn)一步挖掘而產(chǎn)生信息的浪費(fèi)。為了充分利用生產(chǎn)過程中收集的大量數(shù)據(jù),實(shí)現(xiàn)全面質(zhì)量管理,應(yīng)用數(shù)據(jù)挖掘技術(shù)(Data Mining,DM)實(shí)現(xiàn)智能化加工過程質(zhì)量診斷控制的技術(shù)和系統(tǒng)成為眾多專家學(xué)者及企業(yè)新的研究熱點(diǎn)。本文在總結(jié)前人研究基礎(chǔ)之上構(gòu)建MES環(huán)境下制造過程質(zhì)量數(shù)據(jù)挖掘平臺(tái),分析加工質(zhì)量相關(guān)數(shù)據(jù),研究具有控制圖模式識(shí)別、質(zhì)量預(yù)測(cè)及診斷功能的質(zhì)量控制系統(tǒng),主要研究?jī)?nèi)容包括以下三方面： (1)針對(duì)制造過程數(shù)據(jù)特點(diǎn),提出一種適用于過程質(zhì)量數(shù)據(jù)分析的不純性度量：Fβ度量類置信度比Fβ-Confidence Proportion,FCP),并建立基于FCP不純性度量的決策樹(Decision Tree, DT)。在構(gòu)建分類器的基礎(chǔ)上,構(gòu)建基于FCP決策樹的控制圖模式識(shí)別系統(tǒng),針對(duì)控制圖數(shù)據(jù)維度較高的現(xiàn)象引用統(tǒng)計(jì)量作為控制圖模式識(shí)別的統(tǒng)計(jì)特征。經(jīng)測(cè)試表明該模式識(shí)別系統(tǒng)精度高,處理速度快,符合質(zhì)量預(yù)警控制的需求。 (2)提出基于FCP決策樹與聚類分析(Cluster Analysis,CA)的質(zhì)量預(yù)測(cè)方法。同時(shí)應(yīng)用兩種數(shù)據(jù)挖掘算法分析不同種控制圖模式下的制造過程質(zhì)量數(shù)據(jù),在獲取聚類信息及異常質(zhì)量歸類的基礎(chǔ)上研究工序質(zhì)量的異常預(yù)測(cè)。通過質(zhì)量影響因素的聚類分析,獲取不同模式下的質(zhì)量影響因素組合,利用決策樹分析現(xiàn)有過程質(zhì)量數(shù)據(jù)實(shí)現(xiàn)質(zhì)量預(yù)測(cè)。 (3)將基于案例推理(Case-Based Reasoning, CBR)的知識(shí)庫引入質(zhì)量診斷系統(tǒng),實(shí)現(xiàn)了診斷知識(shí)的管理與自學(xué)習(xí)功能。通過對(duì)數(shù)控加工中心底座銑削制造過程實(shí)例中信息數(shù)據(jù)的處理,展示了知識(shí)提取與質(zhì)量診斷的全過程；最終建立MES環(huán)境下數(shù)控加工中心質(zhì)量預(yù)測(cè)診斷系統(tǒng),建立質(zhì)量預(yù)測(cè)數(shù)據(jù)庫、基于案例推理的知識(shí)庫及各子系統(tǒng)模塊的人機(jī)交互界面,實(shí)現(xiàn)了控制圖模式識(shí)別、質(zhì)量預(yù)測(cè)與診斷、知識(shí)庫的自學(xué)習(xí)與維護(hù)功能。
[Abstract]:Product quality is a concrete reflection of customer demand and a necessary factor for customer satisfaction. In order to occupy a favorable position in a highly competitive market and improve the competitiveness of enterprises, Modern enterprises must strengthen quality management. With the development of science and technology and the maturation of the market, the market demand for quality has been improved constantly, the existing quality management technology and tools can not meet the demand. The quality data in production process in manufacturing Execution system mes environment is collected by workshop job field control, analyzed and processed by system and engineer and stored in a specified database or data warehouse. In order to make full use of the large amount of data collected in the production process, the accumulation of such data leads to a decline in the ability of the analyst to process the data, and the potential value of the data is not further mined, resulting in a waste of information. Achieve total quality management, The technology and system of intelligent manufacturing process quality diagnosis and control based on data mining technology (DM) has become a new research hotspot for many experts and enterprises. Based on the summary of previous researches, this paper constructs manufacturing under MES environment. Process quality data mining platform, The quality control system with the function of pattern recognition, quality prediction and diagnosis of control chart is studied by analyzing the relevant data of processing quality. The main research contents include the following three aspects:. 1) according to the characteristics of manufacturing process data, a kind of uncertainty metric: F 尾 -confidence ratio F 尾 is proposed for process quality data analysis, and a decision tree based on FCP impureness metric is established. The control chart pattern recognition system based on FCP decision tree is constructed, and the statistical quantity is used as the statistical feature of the control chart pattern recognition for the phenomenon with high dimension of the control chart data. The test results show that the pattern recognition system has high precision and fast processing speed. In line with the quality of early warning control requirements. (2) A quality prediction method based on FCP decision tree and cluster analysis is proposed, and two kinds of data mining algorithms are used to analyze the quality data of manufacturing process under different control chart models. On the basis of obtaining clustering information and classifying abnormal quality, the abnormal prediction of process quality is studied. Through clustering analysis of quality influencing factors, the combination of quality influencing factors under different models is obtained. Using decision tree to analyze the existing process quality data to achieve quality prediction. The knowledge base based on Case-Based reasoning (CBR) is introduced into the quality diagnosis system, and the management and self-learning function of diagnosis knowledge is realized. The whole process of knowledge extraction and quality diagnosis is demonstrated. Finally, the quality prediction and diagnosis system of NC machining center under MES environment, the quality prediction database, the knowledge base based on case-based reasoning and the man-machine interface of each subsystem module are established. The functions of pattern recognition, quality prediction and diagnosis, self learning and maintenance of knowledge base are realized.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13;TB114.2

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