本文選題：拉伸矯直 + 參數(shù)辨識(shí)　； 參考：《河南工業(yè)大學(xué)》2017年碩士論文

【摘要】：鎂合金作為目前已知最輕的合金材料,被廣泛的應(yīng)用于軍工機(jī)械、汽車制造、醫(yī)療器械等領(lǐng)域中。目前針對(duì)鎂合金研究的存在很多欠缺,已研發(fā)成果大多都是初步的和分散的,并沒有將其進(jìn)行統(tǒng)一和標(biāo)準(zhǔn)化。以鎂合金矯直為例,工業(yè)生產(chǎn)中對(duì)鎂合金平直度的矯直雖已取得一些重要的基礎(chǔ)性研究成果,但有關(guān)鎂合金矯直理論的研究仍遠(yuǎn)落后與工程實(shí)際需求,這進(jìn)一步又限制了鎂合金的發(fā)展。本文以基于彈塑性理論構(gòu)建的鎂合金拉伸矯直理論模型為基礎(chǔ),采用不同算法對(duì)拉伸矯直過程中材料性能參數(shù)進(jìn)行在線辨識(shí),以提高材料性能參數(shù)的精確度,進(jìn)而提高矯直位移精度,主要工作如下:1)對(duì)拉伸矯直模型進(jìn)行介紹分析,建立起相應(yīng)的求解模型和算法,利用matlab軟件對(duì)該算法實(shí)施仿真實(shí)驗(yàn),并通過實(shí)例驗(yàn)證該方法的有效性。2)基于遺傳算法優(yōu)化BP網(wǎng)絡(luò)的原理;設(shè)計(jì)針對(duì)矯直模型的網(wǎng)絡(luò)結(jié)構(gòu);通過樣本訓(xùn)練,獲得要辨識(shí)的材料參數(shù)值;并將其與BP神經(jīng)網(wǎng)絡(luò)算法結(jié)果進(jìn)行比較,確認(rèn)優(yōu)化后的網(wǎng)絡(luò)預(yù)測(cè)精度更高。3)進(jìn)一步結(jié)合拉伸矯直理論模型,分別描述基于彈性加載第一、第二階段和彈塑性加載階段的遞推最小二乘辨識(shí)方法;將其預(yù)測(cè)結(jié)果與試驗(yàn)結(jié)果進(jìn)行對(duì)比分析,該方法的辨識(shí)結(jié)果滿足工業(yè)精度。4)研究基于Kalman濾波與擴(kuò)展Kalman濾波的材料參數(shù)聯(lián)合辨識(shí),分別建立基于彈性階段的Kalman濾波(KF)模型和基于彈塑性階段的擴(kuò)展Kalman濾波(EKF)模型,將預(yù)測(cè)結(jié)果進(jìn)行分析。最后對(duì)本文工作進(jìn)行總結(jié),并對(duì)鎂合金矯直過程中材料參數(shù)辨識(shí)方法研究作了進(jìn)一步的展望和分析。
[Abstract]:Magnesium alloys as the lightest known alloy materials are widely used in military machinery, automobile manufacturing, medical devices and other fields.At present, there are many deficiencies in the research of magnesium alloys, most of the research and development results are preliminary and scattered, and it has not been unified and standardized.Taking magnesium alloy straightening as an example, although some important basic research results have been obtained in the industrial production of magnesium alloy leveling, the research on the theory of magnesium alloy straightening still lags far behind and the practical engineering needs are still far behind.This further limits the development of magnesium alloys.Based on the theoretical model of tensile straightening of magnesium alloy based on elastic-plastic theory, different algorithms are used to identify the material property parameters in order to improve the accuracy of the parameters.And then improve the accuracy of straightening displacement. The main work is as follows: 1) introduce and analyze the stretching straightening model, set up the corresponding solution model and algorithm, and use matlab software to carry out the simulation experiment of the algorithm.The effectiveness of the method is verified by an example. 2) the principle of optimizing BP neural network based on genetic algorithm, the network structure for straightening model, the material parameter value to be identified are obtained by sample training.Compared with the BP neural network algorithm, it is confirmed that the optimized neural network has higher prediction accuracy. 3) further combining with the stretch straightening theory model, the first one based on elastic loading is described, respectively.The recursive least square identification method for the second stage and the elastic-plastic loading stage is used, and the predicted results are compared with the experimental results.The identification results of this method satisfy the industrial precision .4) the joint identification of material parameters based on Kalman filter and extended Kalman filter is studied. The Kalman filter model based on elastic stage and the extended Kalman filter model based on elastic-plastic stage are established, respectively.The forecast results are analyzed.Finally, the work of this paper is summarized, and the research of material parameter identification in the straightening process of magnesium alloy is further prospected and analyzed.
【學(xué)位授予單位】：河南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG339;TP18

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本文編號(hào)：1760383