本文選題：數(shù)控機(jī)床 + 運(yùn)動(dòng)精度��； 參考：《重慶理工大學(xué)》2017年碩士論文

【摘要】：精度是數(shù)控機(jī)床的一個(gè)重要性能指標(biāo)。數(shù)控機(jī)床精度直接影響加工零件的精度和質(zhì)量,體現(xiàn)著整個(gè)制造業(yè)的技術(shù)水平和競(jìng)爭(zhēng)力。我國(guó)是數(shù)控機(jī)床消費(fèi)大國(guó),但不是數(shù)控機(jī)床制造強(qiáng)國(guó),需求和制造力處于不平衡狀態(tài),尤其是高檔數(shù)控機(jī)床。與進(jìn)口數(shù)控機(jī)床相比,國(guó)產(chǎn)數(shù)控機(jī)床的精度保持性不高,提高精度保持性是國(guó)內(nèi)數(shù)控機(jī)床亟待解決的問(wèn)題。本文針對(duì)數(shù)控機(jī)床運(yùn)動(dòng)精度進(jìn)行了分析和預(yù)測(cè),根據(jù)分析和預(yù)測(cè)結(jié)果,可在數(shù)控機(jī)床運(yùn)動(dòng)精度失效之前采取措施減小或消除誤差來(lái)源,從而提高產(chǎn)品加工精度,并延長(zhǎng)數(shù)控機(jī)床的服役時(shí)間,提高精度保持性。另外還能指導(dǎo)機(jī)床維修工作,防止失修帶來(lái)的經(jīng)濟(jì)損失。本文針對(duì)數(shù)控機(jī)床的多個(gè)運(yùn)動(dòng)精度特征量時(shí)間序列,基于多元相空間技術(shù)建立運(yùn)動(dòng)精度相空間,將低維時(shí)間序列映射到高維相空間中,恢復(fù)混沌吸引子,揭露復(fù)雜表象后的有序狀態(tài)。引入主成分分析法對(duì)重構(gòu)后的相空間進(jìn)行去冗降維,簡(jiǎn)化模型結(jié)構(gòu)。然后構(gòu)建小波神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型,以運(yùn)動(dòng)精度的相點(diǎn)坐標(biāo)為輸入,圓度誤差變化量為輸出,對(duì)預(yù)測(cè)模型訓(xùn)練學(xué)習(xí),從而實(shí)現(xiàn)數(shù)控機(jī)床運(yùn)動(dòng)精度的預(yù)測(cè)。本文主要的研究工作如下:(1)搭建實(shí)驗(yàn)平臺(tái)測(cè)試數(shù)控機(jī)床運(yùn)動(dòng)精度,獲取多個(gè)運(yùn)動(dòng)精度特征量時(shí)間序列數(shù)據(jù)。并通過(guò)算數(shù)平均法對(duì)數(shù)據(jù)進(jìn)行降噪處理,采用最大李雅普諾夫指數(shù)分析數(shù)控機(jī)床運(yùn)動(dòng)精度序列的混沌特性。(2)重構(gòu)數(shù)控機(jī)床運(yùn)動(dòng)精度相空間。采用C-C算法分別計(jì)算出各運(yùn)動(dòng)精度特征量時(shí)間序列的相空間重構(gòu)參數(shù)。然后通過(guò)主成分分析法濾除冗余信息,簡(jiǎn)化相空間結(jié)構(gòu)。以運(yùn)動(dòng)精度相空間維數(shù)作為小波神經(jīng)網(wǎng)絡(luò)輸入層神經(jīng)元數(shù)目,避免了試湊法,保證了輸入信息的完備性。(3)構(gòu)建數(shù)控機(jī)床運(yùn)動(dòng)精度小波神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型。選用Morlet小波作為隱含層神經(jīng)元激勵(lì)函數(shù),分析并確定小波神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),給出了預(yù)測(cè)模型參數(shù)修正算法,然后通過(guò)重構(gòu)后的運(yùn)動(dòng)精度相空間數(shù)據(jù)對(duì)預(yù)測(cè)模型進(jìn)行訓(xùn)練和預(yù)測(cè)。最后根據(jù)預(yù)測(cè)評(píng)價(jià)指標(biāo)(預(yù)測(cè)精度、最大相對(duì)誤差、相對(duì)均方誤差),對(duì)結(jié)果分析評(píng)價(jià)。同時(shí)與未降維處理的多元WNN預(yù)測(cè)模型、單元WNN預(yù)測(cè)模型進(jìn)行比較。實(shí)驗(yàn)結(jié)果表明:基于多元相空間重構(gòu)的數(shù)控機(jī)床運(yùn)動(dòng)精度預(yù)測(cè)模型的預(yù)測(cè)精度高,且經(jīng)降維處理的多元預(yù)測(cè)模型所得相對(duì)均方誤差比其他兩個(gè)模型低了一個(gè)數(shù)量級(jí)。說(shuō)明了本文提出的預(yù)測(cè)模型能夠有效跟蹤數(shù)控機(jī)床運(yùn)動(dòng)精度變化規(guī)律。
[Abstract]:Precision is an important performance index of NC machine tools. The accuracy of NC machine tools directly affects the precision and quality of machining parts, and reflects the technical level and competitiveness of the whole manufacturing industry. China is a large consumer of CNC machine tools, but not a powerful country in NC machine tool manufacturing. The demand and manufacturing force are in an unbalanced state, especially for high-grade NC machine tools. Compared with imported CNC machine tools, the precision retention of domestic NC machine tools is not high, so it is an urgent problem to improve the precision retention of domestic CNC machine tools. In this paper, the motion accuracy of NC machine tools is analyzed and forecasted. According to the results of analysis and prediction, measures can be taken to reduce or eliminate the error sources before the motion accuracy of NC machine tools fails, so as to improve the machining accuracy of products. The service time of NC machine tool is prolonged and the accuracy retention is improved. In addition, it can also guide the maintenance of machine tools to prevent economic losses caused by disrepair. In this paper, the motion precision phase space is established based on the multivariate phase space technique, and the low dimensional time series is mapped to the high dimensional phase space to restore the chaotic attractor. The orderly state of being exposed to a complex representation. Principal component analysis (PCA) is introduced to reduce the dimension of the reconstructed phase space and simplify the model structure. Then the prediction model of wavelet neural network is constructed. The phase coordinate of motion accuracy is taken as input and the variation of roundness error is output. The prediction model is trained and studied so as to realize the prediction of motion accuracy of NC machine tools. The main research work of this paper is as follows: 1) build an experimental platform to test the motion accuracy of CNC machine tools and obtain time series data of multiple motion accuracy characteristic quantities. The maximum Lyapunov exponent is used to analyze the chaotic characteristics of the sequence of motion accuracy of NC machine tools, and the phase space of motion accuracy of NC machine tools is reconstructed. C-C algorithm is used to calculate the phase space reconstruction parameters of each time series of motion accuracy characteristic variables. Then the redundant information is filtered by principal component analysis to simplify the phase space structure. The dimension of phase space of motion precision is used as the number of neurons in the input layer of wavelet neural network, which avoids the trial and error method, and ensures the completeness of input information. The wavelet neural network prediction model of motion accuracy of NC machine tool is constructed. The Morlet wavelet is chosen as the neuron excitation function of hidden layer, the wavelet neural network structure is analyzed and determined, the parameter correction algorithm of the prediction model is given, and then the prediction model is trained and predicted by the reconstructed motion precision phase space data. Finally, according to the prediction evaluation index (prediction accuracy, maximum relative error, relative mean square error), the results are analyzed and evaluated. At the same time, it is compared with the multivariate WNN prediction model and the unit WNN prediction model without dimensionality reduction. The experimental results show that the prediction accuracy of the motion accuracy prediction model of NC machine tools based on multi-phase space reconstruction is high, and the relative mean square error of the multivariate prediction model treated by dimensionality reduction is one order of magnitude lower than that of the other two models. The prediction model presented in this paper can effectively track the movement accuracy of NC machine tools.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG659

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本文編號(hào)：1781299