【摘要】：車身覆蓋件由于形狀復(fù)雜、精度要求高,其沖壓成形過程是一個(gè)復(fù)雜的彈塑性、大變形力學(xué)過程。隨著數(shù)值模擬技術(shù)的高速發(fā)展,已有大量學(xué)者利用數(shù)值模擬技術(shù)對(duì)板料在成形過程中可能出現(xiàn)的缺陷進(jìn)行了預(yù)測(cè),利用該技術(shù)既可以減少試模次數(shù),還能夠優(yōu)化模具的結(jié)構(gòu)設(shè)計(jì)和工藝設(shè)計(jì)。然而,由于汽車覆蓋件外形結(jié)構(gòu)復(fù)雜,對(duì)其質(zhì)量要求較高,這使得單純使用數(shù)值模擬技術(shù)具有較高的盲目性�；ㄙM(fèi)了大量仿真時(shí)間,實(shí)驗(yàn)結(jié)果仍不理想。如何合理使用各種優(yōu)化算法,并將多種算法結(jié)合起來對(duì)沖壓過程中的各個(gè)成形工藝參數(shù)進(jìn)行合理調(diào)整,并獲得滿足質(zhì)量要求的工藝參數(shù)組合,長(zhǎng)期以來都是汽車制造領(lǐng)域的熱點(diǎn)和難點(diǎn)。因此,本文基于GS理論和神經(jīng)網(wǎng)絡(luò)遺傳算法函數(shù)尋優(yōu)法,利用非線性有限元分析軟件Dynaform,將一汽某汽車制造有限公司生產(chǎn)的輕量型卡車左后側(cè)圍外板為研究對(duì)象,對(duì)其沖壓成形中工藝參數(shù)尋優(yōu),以提高其成形質(zhì)量。首先,借助正交試驗(yàn)法,初步獲取在不同工藝參數(shù)組合下的最大減薄率數(shù)值;接著,基于GS理論,對(duì)獲取的數(shù)據(jù)進(jìn)行灰色關(guān)聯(lián)度分析,分析得出影響左后側(cè)外板最大減薄率的兩個(gè)最主要因素即沖壓速度和壓邊力;之后,使用拉丁超立方抽樣法對(duì)選出的兩個(gè)主要因素在既定的范圍內(nèi)進(jìn)行隨機(jī)抽樣,并借助非線性有限元軟件Dynaform對(duì)抽樣結(jié)果逐一仿真分析獲得各自的最大減薄率;然后,基于神經(jīng)網(wǎng)絡(luò)遺傳算法函數(shù)尋優(yōu)模型,將拉丁超立方抽樣獲得數(shù)據(jù)中的沖壓速度和壓邊力作為輸入,最大減薄率作為輸出,用輸入輸出數(shù)據(jù)訓(xùn)練BP神經(jīng)網(wǎng)絡(luò);用遺傳算法尋優(yōu)把訓(xùn)練后的BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)結(jié)果作為個(gè)體適應(yīng)度值計(jì)算的初始值,并進(jìn)行個(gè)體適應(yīng)度值計(jì)算即預(yù)測(cè)得到最大減薄率最小的結(jié)果,并得到對(duì)應(yīng)輸入值。最后,運(yùn)用Dynaform軟件對(duì)輸出的最優(yōu)工藝參數(shù)組合進(jìn)行仿真驗(yàn)證,借助該公司的實(shí)驗(yàn)平臺(tái)進(jìn)行實(shí)驗(yàn)驗(yàn)證。通過對(duì)比優(yōu)化前后的有限元仿真結(jié)果和實(shí)驗(yàn)結(jié)果可知,優(yōu)化后的拉延成形工藝參數(shù)能夠改善板料的成形性能。采用此方法可以有效的預(yù)測(cè)和優(yōu)化汽車覆蓋件成形工藝參數(shù)。
[Abstract]:Due to complex shape and high precision, the stamping process of body panel is a complicated elastoplastic and large deformation mechanical process. With the rapid development of numerical simulation technology, a large number of scholars have made use of numerical simulation technology to predict the possible defects in sheet metal forming process. Also can optimize the mold structure design and process design. However, due to the complexity of the shape and structure of the automobile panels, the quality of the panels is very high, which makes the use of numerical simulation technology more blind. A lot of simulation time has been spent and the experimental results are still not satisfactory. How to reasonably use all kinds of optimization algorithms and combine them together to adjust each forming process parameter reasonably and obtain the combination of process parameters to meet the quality requirements. For a long time, it has been a hot and difficult point in the field of automobile manufacturing. Therefore, based on GS theory and neural network genetic algorithm function optimization method, this paper uses nonlinear finite element analysis software Dynaform, to study the left rear side outer panel of light weight truck produced by FAW Automobile Manufacturing Co., Ltd. The process parameters are optimized to improve the forming quality. Firstly, the maximum thinning rate of different process parameters is obtained by orthogonal test. Then, based on GS theory, grey correlation degree analysis of the obtained data is carried out. The results show that the two most important factors affecting the maximum thinning rate of the left and posterior outer plate are the stamping speed and the blank holder force, and then the Latin hypercube sampling method is used to sample the selected two main factors randomly within a given range. With the help of the nonlinear finite element software Dynaform, the sample results are simulated one by one to obtain the maximum thinning rate, and then, based on the neural network genetic algorithm function optimization model, The punching speed and blank holding force in the data obtained by Latin hypercube sampling are taken as input, the maximum thinning rate is taken as output, and the input and output data are used to train BP neural network. Genetic algorithm is used to optimize the predicted results of the trained BP neural network as the initial value of the individual fitness calculation, and the individual fitness value is calculated, that is, the maximum thinning rate and the minimum thinning rate are predicted and the corresponding input values are obtained. Finally, Dynaform software is used to simulate the optimal process parameters, and the experiment platform of the company is used to verify the optimal process parameters. By comparing the finite element simulation results and experimental results before and after optimization, it can be seen that the optimized drawing process parameters can improve the forming performance of sheet metal. This method can effectively predict and optimize the forming process parameters of automobile panels.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG386;U466

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 覃建周,吳伯杰;汽車覆蓋件沖壓方向的優(yōu)化[J];模具工業(yè);2002年02期

2 沈啟，

本文編號(hào)：2247208