Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1385-1391.doi: 10.13229/j.cnki.jdxbgxb20180797

   

Multi material optimal design of vehicle product using polylactic acid composites

Fang-wu MA1,2(),Lu HAN1,2,Yang ZHOU3,Shi-ying WANG4,Yong-feng PU1,2()   

  1. 1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
    2. Qingdao Automotive Research Institute, Jilin University, Qingdao 266000, China
    3. FAW Vehicle Development Department, Changchun 130031, China
    4. China FAW Group CO. , LTD. Product Development and Research Center Vehicle Test Department, Changchun 130031, China
  • Received:2018-07-30 Online:2019-09-01 Published:2019-09-11
  • Contact: Yong-feng PU E-mail:mikema@jlu.edu.cn;puyongfeng@jlu.edu.cn

Abstract:

In this paper, basalt fiber was used to improve the tensile properties to satisfy the requirement in automotive design. Meanwhile, Multi material design was applied in this case to achieve lightweight. The relationship between the fiber weight and the tensile properties was studied. Operation condition was obtained from reliability test in the proving ground using as constraints in Multi material design. The optimal process with combination of Multi-island Genetic Algorithm (MIGA) and Genetic Algoritm—Response Surface Method (GA-RSM) was used to search the optimal solution. The results show that basalt fiber has positive effect on improving the tensile properties. With the Multi material design and optimal process, the mass of the product is reduced by near 9%. It can be concluded that PLA composites is suitable for replacing the petroleum based materials in automotive and lightweight can be achieved with the combination of multi material design and MIGA.

Key words: vehicle engineering, polylactic acid composites, multi material design, multi-island genetic algorithm(MIGA), genetic algorithm?response surface methodology(GA-RSM)

CLC Number: 

  • U41

Fig.1

Configuration for tensile testing specimen"

Table 1

Properties of PLA/BF with different content"

纤维含量/%密度/(g·cm-3)拉伸强度/MPa弹性模量/MPa变异系数/%
01.25072.01384.64.4
101.37589.12209.65.9
201.500109.62878.71.9
301.625124.33742.94.2
401.750138.24362.53.9
501.875140.85005.92.8
602.000136.14350.53.1

Fig.2

SEM of PLA with different content BF"

Fig.3

Fitting curve and original points of tensile modulus"

Table 2

Predictive value of PLA/BF"

纤维含量/%密度/(g·cm-3)弹性模量/MPa
01.25001384
11.26251486
21.27501586
31.28751680
41.30001769
51.31251852
61.32501932
???
581.97504546
591.98754430
602.00004294

Fig.4

Process of load spectrum analysis"

Fig.5

FFT(0-50 Hz)PSD"

Fig.6

Design variable of optimization"

Fig.7

Evolution procedures between two adjacent generations in MIGA"

Table 3

Detailed parameters of MIGA"

参 数数值
Sub?population size10
Number of Islands16
Number of Generations16
Rate of crossover1.0
Rate of migration0.01
Interval of migrations5
Penalty multiplier1000
Penalty exponent2
Maximum failed runs5

Table 4

Sample points and test points"

样本No.x1x2x3x4y1y2y3y4y5
拟合1351737392397.285.80.51155
2281055542318.176.470.59142
3164248532604.73.80.58183
??????????
测试51365636522585.554.320.74181
??????????
6110485272754.573.880.65185

Table 5

Predictive accuracy of two models"

响应预测精度
R2RSME
y10.9765.6608
y20.9860.3335
y30.9830.267
y40.9890.0204
y50.9763.25

Table 6

optimized design variable and responses"

参 数x1x2x3x4y1y2y3y4y5
优化20.217.221.04.62298.26.70.64157
FEA20172152298.06.70.65155
误差/%02111
约束8.36.80.6750
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