轉向架構架外文翻譯--基于確保疲勞強度和減輕重量的轉向架構架設計

1、<p><b>　　中文3090字</b></p><p>　　Bogie frame design in consideration of fatigue strength and weight reduction</p><p>　　B H Parkand K Y Lee</p><p>　　School of Mechanica

2、l Engineering,Yonsei University,Seoul,Republic of Korea</p><p>　　The manuscript was received on 8 April 2005 and was accepted after revision for publication on 25 November 2005.</p><p>　　DOI: 10

3、.1243/09544097F01405</p><p>　　Abstract: In the development of a bogie, the fatigue strength of a bogie frame is an important design criterion. In addition, weight reduction is required in order to save energ

4、y and material .In this study, the fatigue analysis of a bogie frame by using the finite-element method is performed for various loading conditions according to the UIC standards and it is attempted to minimize the weigh

5、t of the bogie frame by artificial neural network and genetic algorithm.</p><p>　　Keywords: bogie, strength, fatigue analysis, neural network, optimization.</p><p>　　1 INTRODUCTION</p>&l

6、t;p>　　A bogie in a train is a very important structural component loaded by various forces in the rail way vehicle motion. The motion of a railway vehicle is affect by the geometry of the track, the interaction betwee

7、n wheels and rails, the suspension, and the inertias of component part s. In the meantime, the weight of a bogie structure should be as light as possible at higher running speed. Therefore, the strength of the bogie shou

8、ld be carefully calculated and analysed by the international standard</p><p>　　In addition, the bogie has a large proportion of the total weight of a vehicle. Savings of energy and material are currently des

9、ign drivers towards lightweight vehicle constructions. In CAE product</p><p>　　design step, optimization for weight reduction and application of the optimal algorithm can make the light weight and the constr

10、aint conditions for the fatigue strength satisfy.</p><p>　　It is a typical structural optimization problem to minimize the weight of the bogie under the fatigue constraint, but the problem cannot be solved b

11、y simply applying the existing numerical optimization algorithms. In the problem, the fatigue constraint is</p><p>　　not expressed as an analytical function in terms of the design variables.</p><p

12、>　　In this article, the FE model of the bogie frame is constructed to simulate the fatigue test. The bogie that is used in this study is composed of welded frame, bolster, self-steering mechanism, primary suspension,

13、secondary suspension, and disc brake system. The fatigue strength of the bogie frame is estimated by the international standard UIC615-4 ‘Motive Power Units Bogies and Running Gear Bogie Frame Structure Strength Test’. T

14、he optimization problem is composed of an object function for the </p><p>　　2 STRESS ANALYSIS OF THE BOGIE FRAME</p><p>　　2.1 FE model of the bogie frame</p><p>　　In this study, the

15、 analysed bogie frame is a bolster type bogie (Fig. 1(a)). The numerical analysis by the FE method is performed to evaluate the fatigue strength of the bogie frame except the bolster.</p><p>　　The bogie is m

16、odelled using shell and solid elements and the FE model is shown in Fig.1(b ).The trailer bogie frame is meshed to have 28, 251 nodes, 23,870 rectangular shell elements, and 2710 hexagonal solid elements.</p><

17、p>　　Considering the boundary conditions of the trailer bogie frame for the primary suspension, the spring boundary elements are established and the stiffness of the elements is the same as the primary suspension. Ther

18、e are 12 spring elements for the primary suspension in the trailer bogie frame. The software programs used are Altair Hyper Mesh and ABAQUS .</p><p>　　The material used in the bogie frame is SWS490A defined

19、in reference [ 2 ] and the material properties are shown in Table 1.</p><p>　　2.2 Load conditions and the evaluation of fatigue strength of the bogie frame</p><p>　　The fatigue analysis is based

20、 on the UIC standard. The main in- service load case is designed to verify the absence of any risk of fatigue cracks occurring under the combined effect of the main forces encountered during service. The load case consis

21、ts of different load scenarios for the bogie frame involving experiencing straight track, curve negotiation, rolling and bouncing effects, and track twist. Tables 2 and 3 show each load and application area and load scen

22、arios.</p><p>　　In Table 2, mv(kg) is the empty mass of vehicles, nb the number of bogie, mþ(kg) the bogie mass, C1(kg)the passenger mass per seat, and g (m/s) and the acceleration due to gravity. From

23、Table 2 and UIC 615-4 [1], the load cases of main in-service are defined in Table 3. Variables Fz1, Fz2, Fy, Ft1, Ft2 of the loads applied during the fatigue strength analysis are presented in Fig.1(a).</p><p&

24、gt;　　At each node, the stress resulted from each load case of Table 3 is determined. From the result, the maximum values max and the minimum values min defined in reference [5] are determined by the steps shown in Fig.2.

25、 From max and min, the mean stress m and the stress amplitudes a are defined as follows</p><p>　　From m and a at each node, the Goodman diagram shown in Fig.3 is obtained. Stress amplitudes have to satisfy t

26、he following equation</p><p>　　The permissible stress p is obtained from the Goodman diagram and n is the design factor. The analysis result of the initial design in Tab le 4 is presented in Fig.3. As shown

27、in Fig.3, the fatigue</p><p>　　strength satisfies mostly the Goodman diagram. At welding and grinding locations, some nodes violate the Goodman diagram. The maximum value of n is 1.04.</p><p>　　

28、3 OPTIMAL DESIGN OF THE BOGIE FRAME</p><p>　　3.1 ANN for the design factor n</p><p>　　The constraint for the fatigue strength is approximated by a three -layered error back propagation neural BP

29、N network. Neural networks are developed to model the way in which the human brain performs</p><p>　　a particular task or processes information [6]. The use of ANN to approximate the functions with a high de

30、gree of non-linearity is well established.</p><p>　　Back propagation is a general-purpose learning algorithm. Considering the fatigue strength of the bogie frame, the design factor n is chosen as the output

31、parameter, whereas the upper cover plate, the lower cover plate, and the inner vertical cover plate of the side frame are chosen as the input parameters of the network. Table 4 and Fig.4 show each design variable and ini

32、tial, upper , and lower values with the initial weight.</p><p>　　First, the fatigue analysis data have to be obtained when approximated by an ANN. The three-level full-factorial design for three design varia

33、bles is used in order to generate training data sets. Each variable has initial, lower, and upper values, as shown in Table 4. The reliability of approximate model is verified by the three test data sets.</p><

34、p>　　The specific nodes to be used in BPN are selected at the locations with the severest values through the experiments of 27 times. These nodes are presented in Fig.5.</p><p>　　In this study, the neural

35、network model of Fig.6 is applied to approximate the design factor at each node.</p><p>　　The approximate results for the design factor at the selected nodes are marked in Table 5. As the maximum percentage

36、error between the test data and the fatigue analysis data is 5.34 percent, it is concluded that the training of the model is successfully accomplished.</p><p>　　3.2 Genetic algorithm for optimization</p&g

37、t;<p>　　Genetic algorithms (GAs) are the search algorithms based on the mechanics of natural selection and natural genetics. GA would make the combination convergence solutions that are globally optimal or</p&g

38、t;<p>　　nearly so, and it has been successfully applied to a variety of some functional optimization problems. As the population size increases, the algorithms find a better solution. However, a bigger population

39、size requires more computational time to find the optimum solution. For this reason, Goldberg [7, 8] proposed serial GA (SGA), which used a small population size when compared with conventional GAs.On the basis of SGAs,

40、Krishnakumar [9] proposed mGAs in 1989.</p><p>　　In this study, the mGA with a population size of five individuals is used. The flow chart of mGA used in this study is illustrated in Fig.7.</p><p&

41、gt;　　The optimization problem is defined as follows </p><p>　　where F(X)(kg) is the weight of the plates, N the number of design variables, A() the area of the ith plate, and Xi(mm) the thickness of the ith

42、plate. The density ρ of SWS490A is 7.85×10 kg/</p><p>　　Figure 8 shows the optimization process of the object function F(X) using mGA. Three different simulations were performed with errors , 0.5 percen

43、t . The optimum value of the best result is 0.504 ton (Table6). The weight of the bogie frame was reduced by 4.7 percent than the initial design. The constraint condition n becomes 1.000 at node 4862 (Table 7). Between t

44、he values of the optimising results and the values of the analysed results by the optimal values of design variables, the maximum erro</p><p>　　Table 7 shows that the optimal value of the approximated ANN mo

45、del satisfies the modified Goodman diagram, but in the actual calculation with optimal thickness, the Goodman diagram is violated at node 4862. This is due to the error between the prediction and analysis models.</p&g

46、t;<p>　　4 CONCLUSIONS</p><p>　　In this study, the fatigue strength of the bogie frame was estimated by the UIC in the developing step of the bogie. In this process, the post-process was developed. The

47、n, the weight reduction problem of the bogie frame was solved .</p><p>　　The fatigue strength was not satisfied by the design conditions of the modified Goodman diagram. However, after the optimization to re

48、duce the weight of the bogie frame was performed, the weight was reduced by 4.7 percent than the initial design . In the process , the BPN network and the GA were used.</p><p>　　The approximated model satisf

49、ies the design constraint, but the analysis result of the optimal design violates the design constraint because of the error between prediction and analysis models.</p><p>　　REFERENCES</p><p>　　

50、1. International Union of Railways. Motive power units, bogies and running gear, bogie frame structure strength tests. UIC 615-4, 1994.</p><p>　　2. Japanese Industrial Standard. Truck frames for railway roll

51、ing stock general rules for design. JIS E4207, 1992.</p><p>　　3. Dietz, S., Netter, H., and Sachau, D. Fatigue life prediction of a railway bogie under dynamic loads though simulation. Veh. Sys. Dyn., 1998,

52、29, 385-402.</p><p>　　4. Oyan, C. Structural strength analysis of the bogie frame in Taipei rapid transit systems. Proc. Instn Mech. Engrs,</p><p>　　Part F: J. Rail and Rapid Transit, 1998, 212

53、(F3), 253-262.</p><p>　　5. European Rail Research Institute. Programme of tests to be carried out on wagons with steel under frame and body (suitable for being fitted with the automatic buffing and draw coup

54、ler) and on their cast steel frame bogies.</p><p>　　ERRI B12/RP 17, 7th edition, 1993.</p><p>　　6. Hagan, M. T. Neural network design, 1996 (PWS Publish Company, Boston, MA).</p><p>

55、;　　7. Goldberg, D. E. Sizing populations for serial and parallel genetic algorithms. Proceeding of the 3rd International Conference on Genetic algorithms, Arlington, VA, 1989, pp. 70 – 79 (Morgan Kaufmann).</p>&l

56、t;p>　　8. Goldberg, D. E. Genetic algorithms in search, optimization and machine learning , 1989 (Addison-Wesley, Boston, MA).</p><p>　　9. Krishnakumar, K. Micro-genetic algorithm for stationary and non-st

57、ationary function optimization. SPIE, Intell. Control Adapt. Syst., 1989, 1196, 282-296.</p><p>　　基于確保疲勞強度和減輕重量的轉向架構架設計</p><p>　　B.H.Park and K.Y.Lee</p><p>　　機械工程學院，延世大學，首爾，韓國.<

58、/p><p>　　這份手稿是于2005年4月8日收到后接受修改,出版于2005年11月25日。</p><p>　　DOI: 10.1243/09544097F01405</p><p>　　摘要：在一個轉向架的設計發(fā)展過程中，轉向架構架疲勞強度的影響是一個重要的設計準則。此外,為了節(jié)約能源和材料需要減輕重量。在這項研究中，用有限元方法在各種加載條件下對轉向架構架進行疲

59、勞分析是根據(jù)UIC的標準形成的，這種方法試圖通過人工神經(jīng)網(wǎng)絡和遺傳算法來減小轉向架構架的重量。</p><p>　　關鍵詞：轉向架、強度、疲勞強度分析、神經(jīng)網(wǎng)絡、優(yōu)化。</p><p><b>　　1 簡介：</b></p><p>　　轉向架是列車上一個非常重要的構件，它承載著鐵道車輛在運動中的各種力。鐵道車輛的運動受到軌道的幾何形狀、輪軌相

60、互作用、懸掛裝置和零部件的慣性力的影響。同時，一臺高速運行列車的轉向架結構的重量應該盡可能輕。因此，轉向架的強度應該在國際標準如UIC[1]和JIS [2]的基礎上仔細地進行計算分析，以獲得一個合理的設計方案。在過去的設計過程里，諸如一些試驗，現(xiàn)場測試，并對原型改進得到一個合理的設計等步驟需要許多時間和很高的成本。然而，在計算機輔助工程(CAE)產(chǎn)品設計中，應用有限元分析方法(FE)可以減少所需的成本和時間。利用有限元分析方法研究轉向架

61、構架曾有幾次先例[3,4]。</p><p>　　此外，轉向架占車輛總重量的一大部分。目前設計者在節(jié)省能源和材料的驅動下對車輛的結構進行輕量化設計。在CAE產(chǎn)品設計步驟，降低重量的優(yōu)化方案以及最優(yōu)算法的應用可使重量減輕并滿足約束條件的疲勞強度。</p><p>　　這是一個典型的疲勞約束下降低轉向架重量的結構優(yōu)化問題,但只是應用現(xiàn)有數(shù)控優(yōu)化算法，問題是無法解決的。在這一問題上，疲勞約束作為

62、一種分析不表達功能方面的設計變量。在這篇文章中，建立轉向架構架的有限元模型是為了模擬疲勞試驗。這項研究中使用的轉向架是由焊接構架、搖枕、自導向機制、一系懸掛、二系懸掛和盤形制動裝置組成。轉向架構架疲勞強度的評估則根據(jù)國際標準UIC615-4 進行“動力單位轉向架和運行齒輪轉向架構架結構強度試驗”。該優(yōu)化問題是由一個降低轉向架重量的對象函數(shù)和約束條件下的疲勞設計標準構成。近似疲勞的約束的人工神經(jīng)網(wǎng)絡(ANN)函數(shù)和微觀遺傳算法(µ

63、;GA)被用來解決這一優(yōu)化問題。</p><p>　　2轉向架構架的應力分析</p><p>　　2.1轉向架構架的有限元模型</p><p>　　在這項研究中，分析對象是搖枕轉向架（圖1(a)）。對轉向架構架有限元模型數(shù)值分析的目的是進行除搖枕外轉向架疲勞強度的評估，拖車轉向架構架是由28250個節(jié)點、23870個矩形殼單元、2710個六角形固體單元緊密連接而成。

64、考慮到拖車轉向架構架一系懸掛的邊界條件，彈簧邊界元件就可以建立，并且這些元件的剛度與一系懸掛完全相同。拖車轉向架構架有12根彈簧為基本懸掛單元。軟件程序使用Altair Hyper Mesh和ABAQU。</p><p>　　這個轉向架構架材料是SWS490A，定義在文獻[2]，該材料的性能見表1。</p><p>　　2.2負載條件下評價轉向架構架疲勞強度的影響</p>&

65、lt;p>　　疲勞分析是基于暫行標準。</p><p>　　在職載荷主要是為了確保使用時在已考慮到的主要力綜合作用過程中沒有任何發(fā)生疲勞裂紋的風險。載荷產(chǎn)生的情況，包含轉向架構架的各種不同負載、直線軌道、曲線通過、軋制和跳躍的影響、和跟蹤扭曲。表2和3顯示各個負載的負載情況和應用領域。</p><p>　　表1 轉向架構架材料特性（MPa）</p><p>&

66、lt;b>　　表2 主應力情況</b></p><p>　　在表2中， 是空車質量， 是轉向架數(shù)量， 是單個轉向架質量， 是乘客的平均質量，g (m/)為重力加速度。根據(jù)表2和標準UIC 615-4[ 1 ]，主要載荷的情況可由表3進行規(guī)定。疲勞強度分析時使用的變量如 的施加情況列在圖.1(a)中。</p><p>　　在每個節(jié)點， 每種載荷下的應力由表3確定?？梢?/p>

67、從結果看出，表5中定義的最大應力和最小應力的大小由圖2中步驟確定， 根據(jù) ，平均應力 和 應力幅值 由下式定義：</p><p>　　表3主應力載荷（KN）</p><p>　　根據(jù)每一節(jié)點上的，可以得到如圖.3所示的古德曼圖。應力幅值需滿足下式要求：</p><p>　　圖2 最大和最小應力的確定</p><p>　　圖3 修改后

68、的古德曼圖</p><p>　　許用應力 從古德曼圖中獲得，n是設計因數(shù)。表4中的初始設計的分析結果如圖3中所示。如圖3所示，疲勞強度大多滿足古德曼應力圖的要求。在焊接和磨削處，</p><p>　　一些節(jié)點違反古德曼圖。最高值的n是1.04。</p><p>　　3轉向架構架的優(yōu)化設計中</p><p>　　3.1 人工神經(jīng)網(wǎng)絡的設計因數(shù)n

69、</p><p>　　疲勞強度約束為一個三層逼近誤差反向傳播神經(jīng)BPN網(wǎng)絡。神經(jīng)網(wǎng)絡塑造了人腦執(zhí)行某一特定任務或處理信息的方式[6]。人工神經(jīng)網(wǎng)絡的高度非線性逼近函數(shù)得到了很好的利用。反向傳播是一種多用途的學習算法。</p><p>　　考慮轉向架構架的疲勞強度，設計因子n被選擇作為輸出參數(shù)，然而其上蓋板、下蓋板和里面的側架垂直蓋板是被選擇作為輸入?yún)?shù)網(wǎng)絡。</p><

70、p>　　表4和圖4顯示各個設計變量的初始值，上、下限值和最初的重量。</p><p>　　首先，疲勞分析數(shù)據(jù)應該在逼近人工神經(jīng)網(wǎng)絡時獲得。這三個水平的設計變量的全因子設計被用來產(chǎn)生訓練數(shù)據(jù)集。每一個變量如表4所示有初始值和上下限值。三項測試驗證了近似模型的可靠性。</p><p>　　用于BPN的特定的節(jié)點通過27次最嚴格的試驗選擇。這些結點呈現(xiàn)在圖5中。</p>&l

71、t;p>　　在這項研究中，圖6所示的神經(jīng)網(wǎng)絡模型被應用于在每個節(jié)點上逼近設計因子。</p><p>　　表5標記了選定節(jié)點設計因子近似結果。測試數(shù)據(jù)及疲勞分析數(shù)據(jù)之間的最高百分比誤差為5.34%，結果表明，該模型的測試成功完成。</p><p><b>　　表4設計變量和限值</b></p><p><b>　　3.2遺傳算法優(yōu)

72、化</b></p><p>　　遺傳算法(GAs)是一種基于自然選擇力學和自然遺傳學的搜索算法。遺傳算法會結合收斂性解決方案得到全局最優(yōu)或近似解，已經(jīng)被成功運用到各種各樣的一些功能優(yōu)化問題。數(shù)量增加時，算法找到一個更好的解決辦法。然而，一個更大的數(shù)量規(guī)模需要更多的計算時間才能找到最合適的解決方案。因此，戈德堡[7,8]提出了串行遺傳算法(SGA)，它通過與常規(guī)遺傳算法相比一個更小的規(guī)模。在SGA的基礎

73、上，Krishnakumar[9]在1989年提出了µGA。</p><p>　　圖4轉向架構架的設計變量X</p><p><b>　　圖5節(jié)點約束條件</b></p><p>　　在這項研究中，五個個體規(guī)模的µGA得到使用。µGA流程圖如圖7。</p><p>　　該優(yōu)化問題是定義如下&

74、lt;/p><p>　　F( X ) (kg) 是蓋板的重量，N是設計變量的個數(shù)， A () 是第i個的蓋板的面積，(mm)是第i個蓋板的厚度。SWS490A的密度7.85×10 kg/</p><p>　　圖6 3-3-1神經(jīng)網(wǎng)絡模型</p><p>　　表5各項測試中人工神經(jīng)網(wǎng)絡設計因子和疲勞分析的誤差</p><p><b

75、>　　圖8遺傳算法結果</b></p><p>　　圖8顯示目標函數(shù)F(X)的優(yōu)化過程使用µGA。三種不同仿真研究在小于誤差0.5%的范圍進行了。最優(yōu)值的最好結果是0.504噸(表6)。轉向架構架的重量比最初的設計降低了4.7%。約束條件變成節(jié)點4862處的1.000了(見表7)。優(yōu)化結果的值和設計變量最優(yōu)值的分析結果之間最大誤差為2.93%。</p><p>

76、<b>　　表6 優(yōu)化結果</b></p><p>　　表7設計因子預測值與比較值</p><p>　　表7表明神經(jīng)網(wǎng)絡模型的近似最優(yōu)值滿足修改后的古德曼圖，但在實際計算最佳厚度, 古德曼圖節(jié)點4862是不符合的。這是由于預測和分析模型之間的誤差。</p><p><b>　　4 總結</b></p>&l

77、t;p>　　在這項研究中，轉向架構架的疲勞強度是在轉向架發(fā)展步驟中根據(jù)標準UIC估計的。在這個過程中成功采用了商品化研制。然后，轉向架構架降低重量的問題解決了。</p><p>　　疲勞強度并不滿足修改后的古德曼圖表的設計條件。然而，進行了降低轉向架構架重量優(yōu)化后的，重量比最初的設計降低了4.7%。在這個過程中，使用了BPN網(wǎng)絡和GA的方法。</p><p>　　近似模型滿足設計約束

78、，但預測和分析模型之間的誤差導致優(yōu)化設計的分析結果違反了設計約束。</p><p><b>　　參考文獻</b></p><p>　　1 International Union of Railways. Motive power units,bogies and running gear, bogie frame structure strengthtests . U

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