Rapid Prediction of Thermal Properties of Automotive Basalt Fiber Composites Based on Fitting Regression Function

doi:10.12141/j.issn.1000-565X.250122

Abstract

Abstract:

The traditional process of new material development, heavily reliant on extensive experimentation and empirical knowledge, suffers from low efficiency, prolonged cycles, and high costs. Integrating efficient experimental techniques with rapid computational simulation and prediction through intelligent methods can significantly shorten the R&D and engineering application cycle while reducing costs. Chopped basalt fiber-reinforced polylactic acid (BF/PLA) composites, being naturally green, environmentally friendly, and biodegradable, represent an ideal alternative material for automotive interior and exterior components with considerable development potential. In order to study the influence of different fiber parameter ratios on the thermal properties of BF/PLA composites and facilitate rapid development of suitable automotive component materials, this study first conducted experiments on the thermal properties of BF/PLA composites under various fiber parameter ratios. Through data correlation analysis, the effects of different fiber parameter ratios on composite thermal properties were examined. Using F-values from three-factor ANOVA, a fiber parameter quasi-centralization method was proposed, establishing fitted regression functions between glass transition temperature/crystallinity and centralized variables. Based on these regression functions and polynomial guiding functions, new ratios of fiber mass fraction, diameter, and length parameters were incorporated to obtain centralized variables for glass transition temperature and crystallinity under the new parameter ratios. The glass transition temperature and crystallinity of BF/PLA composites with new parameter ratios can be obtained by fitting regression function, and the thermal properties of composites with more fiber para-meter ratios can be predicted by fitting regression function, and the determination coefficients are 0.887 0 and 0.855 1 respectively, with prediction accuracy within practically acceptable engineering ranges. Finite element analysis of thermal performance for a vehicle door inner panel demonstrated slightly superior thermal properties using the optimized composite material selected through the proposed method, along with significantly improved R&D efficiency, validating the effectiveness of the approach. This provides important theoretical guidance and methodological reference for rapid development, cost reduction, material substitution, and green design of future automotive composites.

Key words: basalt fiber, polylactic acid, fitting regression function, thermal property, inner panel of automobile door

CLC Number:

TB332

WANG Tong, MA Yupeng, ZHAO Yang. Rapid Prediction of Thermal Properties of Automotive Basalt Fiber Composites Based on Fitting Regression Function[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(12): 172-182.

Figures/Tables 22

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Thermal performance parameters of composite material obtained from DSC curve"

试样	熔化过程				结晶过程			$X c / %$
试样	$θ g$ /°C	$θ o m$ /°C	$θ p m /$ °C	$∆ H m /$ （J·g^-1 ）	$θ o c$ /°C	$θ p c /$ °C	$∆ H c$ /（J·g^-1 ）	$X c / %$
13-7-10BF/PLA	57.635 1	156.033 9	165.818 3	26.384	119.452 7	111.024 2	-25.742	62.277 1
13-7-20BF/PLA	57.952 6	155.963 9	164.392 3	26.155	119.570 6	111.164 7	-20.241	62.360 2
13-7-30BF/PLA	58.328 3	156.530 5	165.726 3	21.643	119.979 4	111.854 6	-20.338	64.486 9
13-7-40BF/PLA	58.651 9	156.171 2	164.831 0	17.153	120.576 5	112.868 3	-19.556	65.786 7
13-7-50BF/PLA	58.471 7	155.638 8	164.146 7	17.008	122.898 5	114.709 8	-14.515	67.791 4
13-7-60BF/PLA	58.218 8	155.528 4	164.129 4	8.840	123.112 5	114.185 4	-16.035	66.868 2
15-7-10BF/PLA	57.492 2	165.348 2	165.348 2	28.036	121.076 0	111.516 0	-23.931	62.087 2
15-7-20BF/PLA	57.918 5	153.962 0	166.103 0	24.371	119.095 7	110.705 8	-23.424	64.240 5
15-7-30BF/PLA	58.234 3	155.153 5	166.232 0	20.598	119.243 9	111.451 1	-22.550	66.279 5
15-7-40BF/PLA	58.512 3	164.804 3	164.804 3	16.296	119.266 9	111.332 4	-22.220	69.025 0
15-7-50BF/PLA	58.311 4	155.808 0	164.808 7	15.463	120.166 1	111.684 0	-18.034	72.036 5
15-7-60BF/PLA	58.106 2	157.471 4	165.298 1	12.682	123.413 1	115.576 3	-14.020	71.779 5
17-7-10BF/PLA	57.416 8	156.519 3	164.950 4	29.320	120.725 7	112.910 3	-26.193	66.323 7
17-7-20BF/PLA	57.828 0	154.390 0	165.730 7	23.366	119.559 3	110.888 1	-26.332	66.798 3
17-7-30BF/PLA	58.187 0	155.702 3	164.611 7	26.889	119.939 2	111.498 6	-17.884	68.775 7
17-7-40BF/PLA	58.451 1	155.279 4	164.767 0	21.407	119.899 9	111.346 0	-17.710	70.102 1
17-7-50BF/PLA	58.207 7	157.433 6	164.677 2	26.031	120.972 5	113.230 2	-7.832	72.824 3
17-7-60BF/PLA	58.057 0	156.904 2	165.069 5	14.992	131.015 5	127.403 1	-31.522	71.317 2

Table 1

Fig.5

Fig.6

Fig.7

Table 2

Three-factor analysis of variance"

来源	平方和	自由度	均方和	$F$ 值
$D$	$S D$	$s - 1$	$M S D = S D s - 1$	$F D = M S D M S E$
L	$S L$	$t - 1$	$M S L = S L t - 1$	$F L = M S L M S E$
$C$	$S C$	$r - 1$	$M S C = S C r - 1$	$F C = M S C M S E$
$E$	$S E$	$r s t m - 1$	$M S E = S E r s t m - 1$
$T$	$S T$	$n - 1$

Table 2

Table 3

Table 4

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Table 5

Table 6

Fig.13

Fig.14

Fig.15

Fig.16

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来源	平方和	自由度	均方	F值	显著性
D	0.305	2	0.152	0.027 9	<0.001
L	1.825	2	0.912	0.165 1	<0.001
C	7.315	5	1.463	0.264 1	<0.001
误差	0.244	44	0.006
总计	183 796.184	54

来源	平方和	自由度	均方	F值	显著性
D	17.442	2	8.721	5.227	<0.001
L	23.172	2	11.586	8.570	<0.001
C	296.821	5	59.364	43.910	<0.001
误差	59.486	44	1.454
总计	238 361.474	54

参数	数值
质量分数/%	40
体积分数/%	22.2
弹性模量/GPa	22.5
泊松比	0.34
热导率/（W·m^-1·K^-1）	0.10
比热容/（J·kg^-1·K^-1）	1 460
热膨胀系数/（μm·m^-1·K^-1）	52.0
密度/（kg·m^-3）	1 570
体积模量/GPa	25.0
剪切模量/GPa	8.4
屈服强度/MPa	90
抗拉强度/MPa	95