本文選題：統(tǒng)計(jì)過(guò)程控制 + 累積和控制圖��； 參考：《上海交通大學(xué)》2014年碩士論文

【摘要】：為了降低質(zhì)量損失和次品廢品的出現(xiàn)，目前制造業(yè)的發(fā)展需求更高精度的質(zhì)量檢測(cè)，當(dāng)生產(chǎn)過(guò)程出現(xiàn)問(wèn)題時(shí)能夠快速發(fā)現(xiàn)并加以改進(jìn)。質(zhì)量偏移分為兩種形式，一種為質(zhì)量特性值的均值偏移問(wèn)題，代表生產(chǎn)過(guò)程遠(yuǎn)離目標(biāo)值；另一種為方差偏移，代表生產(chǎn)過(guò)程幅度過(guò)大。然而在現(xiàn)代生產(chǎn)過(guò)程中，我們要求生產(chǎn)過(guò)程既要接近目標(biāo)值，又不能有太大的波動(dòng)幅度，因此在大部分情況下都需要同時(shí)監(jiān)控生產(chǎn)過(guò)程中的均值和方差。 傳統(tǒng)的休哈特控制圖在檢測(cè)微小偏移上效果并不明顯，本文將檢測(cè)力度更優(yōu)的累積和控制圖引入聯(lián)合監(jiān)控均值和方差偏移的控制圖方法中，利用馬爾可夫鏈方法來(lái)求得新控制圖的平均鏈長(zhǎng)，對(duì)比文獻(xiàn)中各種控制圖的平均鏈長(zhǎng)數(shù)據(jù)以及更換變量數(shù)值改進(jìn)控制圖。結(jié)果證明新的控制圖無(wú)論在檢測(cè)均值或方差的偏移中都有良好的改進(jìn)效果，，尤其是在小偏移方面有很大的提升。文章在新的模型上繼續(xù)改進(jìn)，引入變動(dòng)抽樣間隔的方法，通過(guò)長(zhǎng)短抽樣間隔設(shè)置，使得檢測(cè)微小偏移的力度進(jìn)一步提高。接著本文考慮到在SPC的監(jiān)控過(guò)程中，有時(shí)候很難預(yù)測(cè)過(guò)程的偏移的大小和規(guī)格，這使得我們?cè)谠O(shè)定監(jiān)控參數(shù)的時(shí)候不能根據(jù)固定偏移量來(lái)優(yōu)化結(jié)果。本文將休哈特和累積和控制圖整合模型應(yīng)用于聯(lián)合監(jiān)控均值和方差偏移的研究中，整合模型在大偏移監(jiān)控中更加敏感，增大了控制圖的應(yīng)用范圍。本文的建模和改進(jìn)研究結(jié)果可以大大提高生產(chǎn)監(jiān)控能力，降低質(zhì)量損失。文章最后對(duì)研究的不足和未來(lái)的發(fā)展做了總結(jié)。
[Abstract]:In order to reduce the quality loss and the appearance of defective products, the development of manufacturing industry requires higher precision quality detection, which can be quickly detected and improved when problems occur in the production process. Mass migration can be divided into two forms, one is the mean deviation of the quality characteristic value, which represents the production process away from the target value, and the other is the variance deviation, which represents the excessive amplitude of the production process. However, in the modern production process, we require that the production process should be close to the target value without too much fluctuation, so in most cases, it is necessary to monitor the mean value and variance in the production process at the same time. The traditional Shewhart control chart is not effective in detecting small migration. In this paper, the cumulative and control chart, which is better for detecting, is introduced into the control chart method of joint monitoring of mean and variance shifts. The average chain length of the new control chart is obtained by using the Markov chain method. The average chain length data of various control charts in the literature and the numerical improvement control charts of changing variables are compared. The results show that the new control chart has a good improvement in the detection of mean and variance migration, especially in the small migration. In this paper, the new model is further improved, and the method of variable sampling interval is introduced. Through the setting of long and long sampling interval, the detection of small offset is further improved. Then this paper considers that it is very difficult to predict the deviation size and specification of SPC in the process of SPC monitoring which makes it impossible for us to optimize the results according to the fixed offset when we set the monitoring parameters. In this paper, Shewhart and cumulative sum control chart integration model are applied to the study of joint monitoring mean and variance migration. The integration model is more sensitive to large migration monitoring and increases the application scope of control chart. The modeling and improving results of this paper can greatly improve the production monitoring ability and reduce the quality loss. At the end of the article, the deficiency of the research and the development of the future are summarized.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F273.2;TB114.2

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本文編號(hào)：2107076