【摘要】：產(chǎn)品質(zhì)量形成并貫穿于整個(gè)產(chǎn)品生命周期,是企業(yè)參于市場(chǎng)競(jìng)爭(zhēng)、賴以生存和發(fā)展的基礎(chǔ),而加工過(guò)程中的產(chǎn)品質(zhì)量是產(chǎn)品最終質(zhì)量的基石。隨著“世界級(jí)質(zhì)量”的提出,市場(chǎng)、客戶、企業(yè)對(duì)質(zhì)量的要求不斷提高,傳統(tǒng)的質(zhì)量控制已不能滿足其需求。 加工過(guò)程中的產(chǎn)品質(zhì)量出現(xiàn)異常,只有很少一部分時(shí)間用于質(zhì)量監(jiān)測(cè)和控制,而80%的時(shí)間都用來(lái)判斷異常的來(lái)源和調(diào)整引起異常的因素,所以為了滿足加工過(guò)程全面質(zhì)量管理的需求,應(yīng)用于加工質(zhì)量的診斷技術(shù)和系統(tǒng)成為眾多學(xué)者和企業(yè)新的研究熱點(diǎn)。本文在前人質(zhì)量控制研究的基礎(chǔ)上,提出了基于改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的控制圖模式識(shí)別,同時(shí)對(duì)加工過(guò)程中的質(zhì)量診斷與調(diào)整進(jìn)行了相關(guān)研究。 主要研究包括三方面的內(nèi)容： (1)提出一種基于激勵(lì)函數(shù)參數(shù)可調(diào)和動(dòng)態(tài)閾值的改進(jìn)BP神經(jīng)網(wǎng)絡(luò)控制圖模式識(shí)別算法,并優(yōu)化Monte Carlo工序數(shù)據(jù)模擬方法,使樣本數(shù)據(jù)更具與實(shí)際生產(chǎn)數(shù)據(jù)相同的質(zhì)量特性。根據(jù)改進(jìn)后的網(wǎng)絡(luò)參數(shù)迭代公式,將預(yù)處理后的樣本數(shù)據(jù)作為輸入對(duì)該神經(jīng)網(wǎng)絡(luò)識(shí)別器進(jìn)行訓(xùn)練,訓(xùn)練結(jié)果用于生產(chǎn)過(guò)程的控制圖模式識(shí)別。改進(jìn)BP神經(jīng)網(wǎng)絡(luò)識(shí)別器的拓?fù)浣Y(jié)構(gòu)簡(jiǎn)單,在保證識(shí)別精度的前提下,提高識(shí)別速度,改善神經(jīng)網(wǎng)絡(luò)的泛化能力。最后,通過(guò)計(jì)算機(jī)模擬驗(yàn)證該算法的可行性。 (2)提出基于故障樹(shù)分析的加工過(guò)程質(zhì)量診斷與調(diào)整方法。首先,對(duì)加工過(guò)程與質(zhì)量相關(guān)的規(guī)則進(jìn)行編碼和產(chǎn)生式知識(shí)表示,系統(tǒng)自動(dòng)生成以控制圖異常模式為頂事件的故障樹(shù),通過(guò)故障樹(shù)分析獲取引起質(zhì)量波動(dòng)的主要異常因素子集。然后,以各質(zhì)量因素特征值的監(jiān)測(cè)結(jié)果作為規(guī)則匹配依據(jù),進(jìn)行專家系統(tǒng)的自動(dòng)推理和人工輔助推理。最后,系統(tǒng)針對(duì)控制圖異常模式診斷結(jié)果進(jìn)行質(zhì)量調(diào)整,將最優(yōu)調(diào)整方案及時(shí)反饋給技術(shù)人員改進(jìn)生產(chǎn)。質(zhì)量診斷與調(diào)整專家系統(tǒng)有助于生產(chǎn)人員快速診斷質(zhì)量異常因素、實(shí)施質(zhì)量調(diào)整方案,大大縮短產(chǎn)品生產(chǎn)周期。 (3)開(kāi)發(fā)了車削加工質(zhì)量診斷與調(diào)整專家系統(tǒng)。采用VB6.0開(kāi)發(fā)環(huán)境和SQL Server 2003數(shù)據(jù)庫(kù)軟件,建立專家系統(tǒng)知識(shí)庫(kù)和各功能模塊的人機(jī)交互界面,實(shí)現(xiàn)加工過(guò)程的控制圖模式識(shí)別、控制圖異常模式的質(zhì)量診斷與調(diào)整、專家知識(shí)庫(kù)的維護(hù)和機(jī)器自學(xué)習(xí)功能。
[Abstract]:Product quality forms and runs through the whole product life cycle. It is the basis for enterprises to participate in market competition and survive and develop. The product quality in the process of processing is the cornerstone of final product quality. With the development of "world-class quality", the demand of market, customer and enterprise for quality has been improved, the traditional quality control can not meet its demand. In the process of manufacturing, the quality of the product is abnormal, only a small part of the time is spent on quality monitoring and control, while 80% of the time is spent on judging the source of the anomaly and adjusting the factors causing it. Therefore, in order to meet the requirement of total quality management in machining process, the diagnostic technology and system applied to machining quality has become a new research hotspot for many scholars and enterprises. Based on the previous research on quality control, this paper puts forward the control chart pattern recognition based on improved BP neural network, and studies the quality diagnosis and adjustment in the process of machining. The main contents are as follows: (1) an improved BP neural network control chart pattern recognition algorithm based on the harmonic dynamic threshold of excitation function parameters is proposed, and the simulation method of Monte Carlo process data is optimized. Make the sample data have the same quality characteristics as the actual production data. According to the improved iterative formula of network parameters, the preprocessed sample data is used as input to train the neural network recognizer, and the training result is used for pattern recognition of control chart in production process. The topology structure of the improved BP neural network recognizer is simple, the recognition speed is improved and the generalization ability of the neural network is improved under the premise of ensuring the recognition accuracy. Finally, the feasibility of the algorithm is verified by computer simulation. (2) A fault tree analysis based process quality diagnosis and adjustment method is proposed. Firstly, the rules related to the quality of the machining process are coded and the knowledge is expressed productively. The system automatically generates the fault tree with the exception pattern of the control diagram as the top event, and obtains the subset of the main abnormal factors that cause the quality fluctuation through the fault tree analysis. Then, the monitoring results of each quality factor's characteristic value are taken as the basis of rule matching, and the expert system's automatic reasoning and artificial assistant reasoning are carried out. Finally, the system adjusts the quality of the abnormal pattern diagnosis result of the control chart, and feedback the optimal adjustment scheme to the technicians to improve the production in time. The quality diagnosis and adjustment expert system is helpful for the production personnel to quickly diagnose the abnormal quality factors, implement the quality adjustment scheme, and greatly shorten the production cycle. (3) an expert system for the diagnosis and adjustment of turning quality is developed. By using VB6.0 development environment and SQL Server 2003 database software, the expert system knowledge base and man-machine interaction interface of each functional module are established, and the control chart pattern recognition and the quality diagnosis and adjustment of the abnormal control chart pattern are realized. Expert knowledge base maintenance and machine self-learning function.
【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】：2011
【分類號(hào)】：TH161.5;TP311.52

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