【摘要】：如何對制造現(xiàn)場獲取的統(tǒng)計數(shù)據(jù)進行分析，方法之一是對制造過程中的質(zhì)量特征值進行統(tǒng)計過程控制。統(tǒng)計過程控制就是對制造過程數(shù)據(jù)的收集、整理、分析，通過客觀定量的方法來分析產(chǎn)品制造過程中的質(zhì)量問題，謀求以較少的資源消耗來提高產(chǎn)品質(zhì)量，是質(zhì)量管理中最有用的工具。在現(xiàn)實的生產(chǎn)過程中，失控的產(chǎn)生是不可避免的，同時造成失控的原因十分復(fù)雜，統(tǒng)計過程控制可以找出制造過程的部分確定性，盡可能保證加工過程的質(zhì)量特征處于一個可以接受的水平之上。當前，在復(fù)雜制造環(huán)境下，由于生產(chǎn)過程高度自動化以及產(chǎn)品的復(fù)雜性，質(zhì)量問題的來源更加廣泛，使要解決如何找出制造過程中的失控原因的問題更加迫切。如何在計算機集成制造的背景下，應(yīng)用統(tǒng)計過程控制自動識別制造過程失控趨勢模式，為用戶提供高效率、高精度的過程受控、失控的信息，并且給出失控原因及針對失控原因的解決辦法是當前研究的一個熱點。 本文從制造過程質(zhì)量穩(wěn)定性出發(fā)，以統(tǒng)計過程控制為背景，探討制造過程中引起失控的各種系統(tǒng)性因素，并結(jié)合發(fā)動機缸體的生產(chǎn)實際，對其制造過程統(tǒng)計數(shù)據(jù)進行模式識別和變點估計，實現(xiàn)了失控過程的真正預(yù)測，為提高制造過程質(zhì)量穩(wěn)定性提供理論依據(jù)與技術(shù)支持。 論文首先總結(jié)了制造過程失控趨勢模式識別及變點估計的經(jīng)典理論，建立了失控趨勢分析的理論體系，提出在統(tǒng)計過程中對失控趨勢采用模糊神經(jīng)網(wǎng)絡(luò)進行模式識別，采用模糊聚類分析來進行變點估計，以有效的提高質(zhì)量穩(wěn)定性。其次建立了控制圖特征的提取方法，設(shè)計基于特征的神經(jīng)網(wǎng)絡(luò)模式識別器，通過對特征的定義完成了由樣本函數(shù)進行的特征提取，根據(jù)不同過程模式的特征，以自動的識別出六種失控模式。然后基于模糊聚類理論以及統(tǒng)計方法，，提出一種新的模糊統(tǒng)計聚類方法來處理實際中的變點問題，并將該方法應(yīng)用于不同類型控制圖中，結(jié)果證明，本文所提出的該方法無論在固定抽樣策略還是可變抽樣策略下，對于控制圖的變點估計都有著良好的效果。并且將基于特征的失控趨勢模式識別和基于模糊聚類的變點估計應(yīng)用于某公司缸體加工過程的質(zhì)量控制中，結(jié)果表明該方法能很好地預(yù)測失控過程。最后基于IDEF和UML開發(fā)了面向制造過程的失控趨勢分析系統(tǒng)OCRS，并將所建立的產(chǎn)品質(zhì)量監(jiān)控模型、數(shù)據(jù)采集等相關(guān)技術(shù)無縫集成，為提高制造過程質(zhì)量穩(wěn)定性提供技術(shù)保障。
[Abstract]:One of the methods to analyze the statistical data obtained from the manufacturing field is to control the quality characteristic values in the manufacturing process. Statistical process control is the collection, collation and analysis of manufacturing process data, and through objective and quantitative methods to analyze the quality problems in the process of manufacturing products, in order to improve the quality of products with less consumption of resources. Is the most useful tool in quality management. In the actual production process, the production of runaway is inevitable, at the same time, the cause of runaway is very complex, statistical process control can find out some certainty of the manufacturing process. Ensure as much as possible that the quality characteristics of the process are above an acceptable level. At present, in the complex manufacturing environment, because of the high automation of the production process and the complexity of the product, the source of the quality problem is more extensive, so it is more urgent to solve the problem of how to find out the cause of the runaway in the manufacturing process. Under the background of computer integrated manufacturing, the statistical process control is applied to identify the trend pattern of manufacturing process runaway automatically, which can provide users with high efficiency, high precision information of process control and out of control. It is a hot topic to give out the cause of runaway and to solve the problem. Starting from the quality stability of manufacturing process and taking statistical process control as the background, this paper discusses various systematic factors that cause runaway in manufacturing process, and combines with the production practice of engine cylinder block. The statistical data of manufacturing process are used for pattern recognition and change point estimation to realize the real prediction of runaway process and to provide theoretical basis and technical support for improving the quality stability of manufacturing process. Firstly, this paper summarizes the classical theory of pattern recognition and change point estimation of runaway trend in manufacturing process, establishes the theoretical system of runaway trend analysis, and puts forward that fuzzy neural network is used for pattern recognition of runaway trend in statistical process. Fuzzy clustering analysis is used to estimate the variation points to improve the quality stability. Secondly, the feature extraction method of control chart is established, and the neural network pattern recognizer based on feature is designed. The feature extraction by sample function is completed by defining the feature, and according to the feature of different process pattern, Automatically identify six out of control modes. Then, based on fuzzy clustering theory and statistical method, a new fuzzy statistical clustering method is proposed to deal with the problem of change points in practice, and the method is applied to different types of control graphs. The method proposed in this paper has a good effect on the change point estimation of the control chart both under the fixed sampling strategy and the variable sampling strategy. The feature based pattern recognition of runaway trend and the change point estimation based on fuzzy clustering are applied to the quality control of cylinder block machining process in a company. The results show that the method can predict the runaway process well. Finally, based on IDEF and UML, a trend analysis system for manufacturing process is developed, and the established product quality monitoring model, data acquisition and other related technologies are seamlessly integrated to provide technical guarantee for improving the quality stability of manufacturing process.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP391.4;TB497

【參考文獻】

相關(guān)期刊論文 前1條

1 陳希孺;變點統(tǒng)計分析簡介[J];數(shù)理統(tǒng)計與管理;1991年01期

相關(guān)博士學(xué)位論文 前4條

1 程志強;基于智能方法的產(chǎn)品制造過程質(zhì)量診斷[D];南京理工大學(xué);2011年

2 呂澤華;模糊集理論的新拓展及其應(yīng)用研究[D];華中科技大學(xué);2007年

3 余建波;基于智能學(xué)習(xí)模型的制造過程質(zhì)量控制理論與方法論研究[D];上海交通大學(xué);2009年

4 施建中;基于模糊聚類的非線性系統(tǒng)辨識研究[D];華北電力大學(xué);2012年

本文編號：2147044