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  本文關(guān)鍵詞：多品種小批量制造模式下的過程質(zhì)量診斷技術(shù)研究　出處：《浙江工業(yè)大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 多品種小批量 過程質(zhì)量診斷 控制圖混合模式 小波分析 PSO-SVM

【摘要】：以浙江省科技廳重大優(yōu)先主題項目“面向服務(wù)架構(gòu)的數(shù)字化設(shè)計與制造關(guān)鍵技術(shù)研究及其在離散制造企業(yè)中的應(yīng)用”為依托,針對多品種小批量生產(chǎn)模式下數(shù)據(jù)樣本少、質(zhì)量診斷困難的問題,以優(yōu)化該生產(chǎn)模式下質(zhì)量診斷方法為目的,擬開展多品種小批量制造模式下的質(zhì)量診斷技術(shù)研究。主要研究工作和成果如下： 1.控制圖混合模式識別。針對多品種小批量生產(chǎn)模式下質(zhì)量數(shù)據(jù)樣本少的問題,同時考慮質(zhì)量過程數(shù)據(jù)常會有多種異常現(xiàn)象混合的情況,提出了小波分析與SVM相結(jié)合的控制圖混合模式識別方法,并將PSO算法引入到SVM中來提高控制圖模式識別的精度,設(shè)計了三層控制圖模式識別模型框架和基本流程。通過構(gòu)造合理的仿真樣本進(jìn)行訓(xùn)練測試,驗證了模型的有效性。 2.控制圖模式參數(shù)估計。為給管理或技術(shù)人員提供質(zhì)量過程調(diào)整的依據(jù),在控制圖模式識別的基礎(chǔ)上,提出了基于PSO-SVM的控制圖模式參數(shù)估計方法,設(shè)計了參數(shù)估計模型框架用于估計三種異常模式的四個參數(shù),并采用仿真實(shí)例驗證了模型的可行性。 3.質(zhì)量異常原因診斷。對幾種質(zhì)量異因診斷方法進(jìn)行了比較,通過借鑒專家系統(tǒng)的知識庫和解釋機(jī)制功能,構(gòu)造相關(guān)數(shù)據(jù)庫,設(shè)計了基于PSO-SVM的質(zhì)量異因診斷模型,以及模型數(shù)據(jù)和用戶可識別內(nèi)容之間的轉(zhuǎn)換規(guī)則。 4.實(shí)例的驗證。在對SJ公司質(zhì)量診斷控制現(xiàn)狀分析的基礎(chǔ)上,將質(zhì)量控制圖混合模式識別、參數(shù)估計、異常原因診斷模型應(yīng)用于SJ公司的質(zhì)量診斷控制中,證明了模型在實(shí)際應(yīng)用中的可行性。
[Abstract]:It is based on the key technology research of digital design and manufacture of service-oriented architecture and its application in discrete manufacturing enterprises. Aiming at the problem of few data samples and difficult quality diagnosis in multi-variety and small-batch production mode, the aim of this paper is to optimize the quality diagnosis method in this production mode. It is planned to carry out the research on the quality diagnosis technology under the multi-variety and small-batch manufacturing mode. The main research work and results are as follows: 1. Mixed pattern recognition of control chart. Considering the problem of few samples of quality data in multi-variety and small-batch production mode, and considering that there are often a variety of abnormal phenomena mixing in the data of quality process. A hybrid pattern recognition method based on wavelet analysis and SVM is proposed, and the PSO algorithm is introduced into SVM to improve the accuracy of control chart pattern recognition. The model framework and basic flow of three-layer control chart pattern recognition are designed, and the validity of the model is verified by training and testing with reasonable simulation samples. 2. Control chart pattern parameter estimation. In order to provide management or technical personnel with the basis of quality process adjustment, on the basis of control chart pattern recognition. A control chart mode parameter estimation method based on PSO-SVM is proposed. A parameter estimation model framework is designed to estimate four parameters of three abnormal patterns. Simulation examples are used to verify the feasibility of the model. 3. Quality abnormal cause diagnosis. Several methods of quality heterogenetic diagnosis are compared, and the related database is constructed by using the functions of knowledge base and explanation mechanism of expert system for reference. A quality heterogeneity diagnosis model based on PSO-SVM is designed, and the conversion rules between model data and user-identifiable content are also presented. 4. Verification of examples. On the basis of analyzing the current situation of quality diagnosis and control in SJ Company, the mixed pattern recognition and parameter estimation of quality control chart are made. The abnormal cause diagnosis model is applied to the quality diagnosis control of SJ Company, which proves the feasibility of the model in practical application.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

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