本文關(guān)鍵詞： 多品種小批量 工序質(zhì)量控制 相似性 模式識別 工序質(zhì)量診斷　出處：《西安電子科技大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：質(zhì)量是現(xiàn)代工業(yè)社會(huì)和各國經(jīng)濟(jì)建設(shè)中一個(gè)受到普遍關(guān)注的突出問題,質(zhì)量在國家的戰(zhàn)略高度、企業(yè)的競爭力和客戶的需求等層面上都有極其重要的意義。研究證明,控制產(chǎn)品質(zhì)量的根本,在于對產(chǎn)品加工過程即生產(chǎn)工序的控制,而非僅對加工成品的檢驗(yàn)。另外,隨著新技術(shù)的不斷涌現(xiàn)和經(jīng)濟(jì)全球化進(jìn)程的推進(jìn),制造企業(yè)的生存環(huán)境發(fā)生了巨大的變化,市場多元化、顧客需求多樣化、零件個(gè)性化使產(chǎn)品更新?lián)Q代的速度不斷增加,多品種小批量生產(chǎn)方式日趨成為主要生產(chǎn)方式。但小批量生產(chǎn)的產(chǎn)品少,樣本少,控制系統(tǒng)具有復(fù)雜性和不穩(wěn)定性,因此無法獲取穩(wěn)定的質(zhì)量特征值。而且,由于質(zhì)量特征數(shù)據(jù)量少,無法滿足經(jīng)典統(tǒng)計(jì)過程控制(SPC)技術(shù)的統(tǒng)計(jì)量要求。本文立足于多品種、小批量制造模式的特點(diǎn),結(jié)合統(tǒng)計(jì)過程控制在產(chǎn)品質(zhì)量控制中的研究與應(yīng)用現(xiàn)狀,以多品種小批量生產(chǎn)制造模式下的工序質(zhì)量控制為目標(biāo),對這種模式下的工序質(zhì)量控制方法進(jìn)行研究,主要研究工作如下： 1.通過對多品種小批量生產(chǎn)的實(shí)際制造過程進(jìn)行分析,提出運(yùn)用相似元理論,構(gòu)建成組工序相似性識別模型,對小批量制造工序進(jìn)行相似性評判,找出符合相似原則的工序,并劃分同組工序,以充分利用工序之間的相似性信息,來拓展樣本空間、增加樣本容量。 2.基于成組相似工序,提出了質(zhì)量特征數(shù)據(jù)轉(zhuǎn)換算法和控制圖的設(shè)計(jì)方法,為控制圖的模式識別奠定了基礎(chǔ)。提出了控制圖基本異常模式的識別方法,并針對目前的控制圖特殊異常模式識別方法適應(yīng)性差、訓(xùn)練慢等問題,提出基于Elman神經(jīng)網(wǎng)絡(luò)的控制圖特殊異常模式識別方法,并對Elman神經(jīng)網(wǎng)絡(luò)進(jìn)行了網(wǎng)絡(luò)設(shè)計(jì)。 3.為了給工序質(zhì)量診斷提供更詳盡的信息,構(gòu)造了3個(gè)相似的BP神經(jīng)網(wǎng)絡(luò)對控制圖特殊異常模式的特征參數(shù)進(jìn)行估計(jì)。在對當(dāng)前常見的工序質(zhì)量診斷方案分析比較的基礎(chǔ)上,將神經(jīng)網(wǎng)絡(luò)技術(shù)引入工序質(zhì)量診斷中,提出使用神經(jīng)網(wǎng)絡(luò)的方法對工序質(zhì)量進(jìn)行診斷,并構(gòu)造了工序質(zhì)量診斷的神經(jīng)網(wǎng)絡(luò)模型。另外,根據(jù)工序質(zhì)量異常原因,提出了工序質(zhì)量控制策略。
[Abstract]:Quality is a prominent problem in modern industrial society and the economic construction of various countries, and quality is in the strategic height of the country. The competitiveness of enterprises and the needs of customers are of great significance. The research has proved that the fundamental to control the quality of products lies in the control of the process of product processing, that is, the production process. In addition, with the continuous emergence of new technologies and the advancement of economic globalization, the living environment of manufacturing enterprises has undergone tremendous changes, market diversification, customer demand diversification. The personalization of parts makes the speed of product renewal increase, and the production mode of multi-variety and small-batch is becoming the main mode of production day by day, but the products produced in small batch are less and the sample is less. The control system is complex and unstable, so it can not obtain stable quality eigenvalue. It can not meet the statistical requirements of classical statistical process control (SPC) technology. This paper is based on the characteristics of multi-variety, small-batch manufacturing model. Combined with the research and application status of statistical process control in product quality control, the process quality control method in this mode is studied, aiming at the process quality control in multi-variety and small-batch manufacturing mode. The main work of the study is as follows: 1. Through the analysis of the actual manufacturing process of multi-variety and small-batch production, the similarity recognition model of group process is constructed by using similarity element theory, and the similarity evaluation of small batch manufacturing process is carried out. In order to make full use of the similarity information between the processes, the sample space can be expanded and the sample size can be increased by finding out the processes that conform to the principle of similarity and dividing them into the same group. 2. Based on the similar working procedure in groups, the algorithm of quality feature data conversion and the design method of control chart are proposed, which lays the foundation for pattern recognition of control chart, and puts forward the recognition method of basic abnormal pattern of control chart. Aiming at the problems of poor adaptability and slow training of current control chart special abnormal pattern recognition methods, a special abnormal pattern recognition method based on Elman neural network is proposed. The Elman neural network is designed. 3. To provide more detailed information for process quality diagnosis. Three similar BP neural networks are constructed to estimate the characteristic parameters of the special abnormal pattern of control chart. This paper introduces neural network technology into process quality diagnosis, proposes a neural network method to diagnose process quality, and constructs a neural network model for process quality diagnosis. In addition, according to the cause of abnormal working procedure quality, the neural network technology is applied to process quality diagnosis. The strategy of process quality control is put forward.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TB114.2;TH16

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