Injection molding of PPO/PA fender based on principal component analysis and grey relational analysis

doi:10.19491/j.issn.1001-9278.2022.05.016

Abstract

Abstract:

This paper reported an intensive study on the injection molding of polyphenylene ether/polyamide fender. Based on the results from numerical simulation， a research method of principal component analysis combined with grey relational analysis was proposed to obtain the first principal component equation. Then， the contribution degree of each index， the influence degree of each process parameter on the comprehensive quality， and the combination of better process parameters were achieved to optimize the defects such as wing warpage deformation， uneven volume shrinkage and shrinkage mark. The results indicated that the influence degree of each process parameter on the comprehensive quality increased by an order of： cooling time > die temperature > melt temperature > holding time > holding pressure. After optimization， the maximum warpage deformation decreased by 9.6 % from 11.29 to 10.21 mm， volume shrinkage decreased by 11.33 % from 16.24 % to 14.4 %， and shrinkage mark length decreased by 7 % from 0.060 6 to 0.056 4 mm.

Key words: fender, principal component analysis, grey relational analysis, multiple target

CLC Number:

TQ320.66⁺2

OUYANG Yu, LIU Hongbin. Injection molding of PPO/PA fender based on principal component analysis and grey relational analysis[J]. China Plastics, 2022, 36(5): 99-103.

Figures/Tables 8

Fig.1. Three?dimensional model of the plastic part

Fig.2. Plastic sample

水平	A/℃	B/℃	C/MPa	D/s	E/s
1	270	115	105	13	16
2	280	125	115	18	18
3	290	135	125	23	20
4	300	145	135	28	22

Tab.1. Test factor level

序号	试验因素					X₁	S_NR（X₁）	X₂	S_NR （X₂）	X₃	S_NR （X₃）
序号	A/℃	B/℃	C/MPa	D/s	E/s	X₁	S_NR（X₁）	X₂	S_NR （X₂）	X₃	S_NR （X₃）
1	280	125	115	18	16	10.20	-20.17	14.92	-23.48	0.056 0	25.04
2	280	145	125	13	20	10.46	-20.39	14.88	-23.45	0.055 2	25.16
3	270	125	125	28	18	10.45	-20.38	14.34	-23.13	0.058 8	24.61
4	290	135	125	23	16	10.16	-20.14	15.38	-23.74	0.045 1	26.92
5	270	115	105	13	16	10.31	-20.27	14.40	-23.17	0.058 9	24.60
6	270	145	115	23	22	15.13	-23.60	12.16	-21.70	0.057 6	24.79
7	290	115	115	28	20	10.11	-20.10	15.40	-23.75	0.047 2	26.52
8	270	135	135	18	20	10.96	-20.80	14.17	-23.03	0.057 4	24.82
9	300	135	115	13	18	10.02	-20.02	15.85	-24.00	0.042 0	27.54
10	280	135	105	28	22	10.34	-20.29	14.89	-23.46	0.055 4	25.13
11	290	145	105	18	18	10.36	-20.31	15.38	-23.74	0.045 7	26.80
12	280	115	135	23	18	10.23	-20.20	14.93	-23.48	0.056 3	24.99
13	300	115	125	18	22	10.53	-20.45	15.88	-24.02	0.045 1	26.92
14	290	125	135	13	22	10.70	-20.59	15.40	-23.75	0.052 1	25.66
15	300	145	135	28	16	9.86	-19.88	15.86	-24.01	0.039 4	28.09
16	300	125	105	23	20	10.07	-20.06	15.87	-24.01	0.043 3	27.27

Tab.2. Orthogonal test and results

序号	翘曲变形量			收缩率			缩痕长度			综合质量GRA
序号	S_NR	Y（X_k ）	$ϕ i k$	S_NR	Y（X_k ）	$ϕ i k$	S_NR	Y（X_k ）	$ϕ i k$	综合质量GRA
1	-20.17	0.08	0.92	-23.48	0.77	0.60	25.04	0.85	0.64	0.85
2	-20.39	0.14	0.83	-23.45	0.75	0.62	25.16	0.81	0.69	0.78
3	-20.38	0.13	0.83	-23.13	0.62	0.87	24.61	0.99	0.51	0.83
4	-20.14	0.07	0.94	-23.74	0.88	0.49	26.92	0.29	0.59	0.84
5	-20.27	0.10	0.88	-23.17	0.63	0.83	24.60	1.00	0.50	0.86
6	-23.60	1.00	0.34	-21.70	0	0.35	24.79	0.93	0.56	0.34
7	-20.10	0.06	0.96	-23.75	0.88	0.49	26.52	0.40	0.72	0.86
8	-20.80	0.25	0.70	-23.03	0.57	1.00	24.82	0.92	0.57	0.76
9	-20.02	0.04	1.00	-24.00	0.99	0.41	27.54	0.13	0.47	0.87
10	-20.29	0.11	0.87	-23.46	0.76	0.61	25.13	0.82	0.68	0.81
11	-20.31	0.12	0.86	-23.74	0.88	0.49	26.80	0.32	0.63	0.78
12	-20.20	0.09	0.91	-23.48	0.77	0.60	24.99	0.87	0.62	0.84
13	-20.45	0.15	0.81	-24.02	1.00	0.41	26.92	0.29	0.59	0.72
14	-20.59	0.19	0.76	-23.75	0.88	0.49	25.66	0.65	1.00	0.71
15	-19.88	0	0.95	-24.01	1.00	0.41	28.09	0	0.40	0.83
16	-20.06	0.05	0.98	-24.01	1.00	0.41	27.27	0.20	0.51	0.86

序号	翘曲变形量			收缩率			缩痕长度			综合质量GRA
序号	S_NR	Y（X_k ）	$ϕ i k$	S_NR	Y（X_k ）	$ϕ i k$	S_NR	Y（X_k ）	$ϕ i k$	综合质量GRA
1	-20.17	0.08	0.92	-23.48	0.77	0.60	25.04	0.85	0.64	0.85
2	-20.39	0.14	0.83	-23.45	0.75	0.62	25.16	0.81	0.69	0.78
3	-20.38	0.13	0.83	-23.13	0.62	0.87	24.61	0.99	0.51	0.83
4	-20.14	0.07	0.94	-23.74	0.88	0.49	26.92	0.29	0.59	0.84
5	-20.27	0.10	0.88	-23.17	0.63	0.83	24.60	1.00	0.50	0.86
6	-23.60	1.00	0.34	-21.70	0	0.35	24.79	0.93	0.56	0.34
7	-20.10	0.06	0.96	-23.75	0.88	0.49	26.52	0.40	0.72	0.86
8	-20.80	0.25	0.70	-23.03	0.57	1.00	24.82	0.92	0.57	0.76
9	-20.02	0.04	1.00	-24.00	0.99	0.41	27.54	0.13	0.47	0.87
10	-20.29	0.11	0.87	-23.46	0.76	0.61	25.13	0.82	0.68	0.81
11	-20.31	0.12	0.86	-23.74	0.88	0.49	26.80	0.32	0.63	0.78
12	-20.20	0.09	0.91	-23.48	0.77	0.60	24.99	0.87	0.62	0.84
13	-20.45	0.15	0.81	-24.02	1.00	0.41	26.92	0.29	0.59	0.72
14	-20.59	0.19	0.76	-23.75	0.88	0.49	25.66	0.65	1.00	0.71
15	-19.88	0	0.95	-24.01	1.00	0.41	28.09	0	0.40	0.83
16	-20.06	0.05	0.98	-24.01	1.00	0.41	27.27	0.20	0.51	0.86

Tab.3. Results of grey relational degree analysis

水平	A	B	C	D	E
1	0.702 5	0.822 5	0.8275	0.8075	0.847 5
2	0.822 5	0.812 5	0.730 0	0.7775	0.832 5
3	0.797 5	0.822 5	0.795 0	0.720 0	0.815 0
4	0.820 0	0.685 0	0.785 0	0.835 0	0.647 5
极差	0.120 0	0.137 5	0.097 5	0.115 0	0.200 0
排序	3	2	5	4	1

Tab.4. Mean range analysis

Fig.3. Factor level effect of mean grey relational analysis

Fig.4. Simulation analysis results

References 8

1	刘成娟，刘成刚，刘艳，等.基于CAE与正交试验法的工艺参数对翘曲变形的影响分析［J］.中国塑料，2018，286（1）：102-107.
	LIU C J， LIU C G， LIU Y， et al. Analysis of effect of injection-molding process parameters on warpage deformation of printer plastic parts based on CAE and orthogonal experiments［J］. China Plastics，2018，286（1）：102-107.
2	梅益，薛茂远，唐芳艳，等.基于极差分析法与GA-ELM的电器连接器壳体注射成型工艺优化［J］.塑料工业，2021，49（1）：75-80.
	MEI Y， XUE M Y， TANG F Y，et al.Optimization of injection molding process of electrical connector shell based on range analysis and GA-ELM［J］.Plastics Industry，2021，49（1）：75-80.
3	黄鹏.基于LM-BP神经网络的汽车AB柱内饰板注塑CAE优化分析［J］.中国塑料，2018，292（7）：137-145.
	HUANG P.CAE optimization design of injection molding of automobile AB column interior trim panel based on LM-BP neural network［J］.China Plastics，2018，292（7）：137-145.
4	刘瑞江，张业旺，闻崇炜，等.正交试验设计和分析方法研究［J］.实验技术与管理，2010，27（9）：52-55.
	LIU R J， ZHANG Y W， WEN C W，et al.Study on the design and analysis methods of orthogonal experiment［J］.Experimental Technology and Management， 2010，27（9）：52-55.
5	段家现，申毅莉，刘兆红.基于层次分析和灰色关联分析的注塑工艺多目标优化［J］.中国塑料，2015，256（7）：80-85.
	DUAN J X， SHEN Y L， LIU Z H. Multi-objective optimization of injection molding process based on AHP and GRA［J］.China Plastics，2015，256（7）：80-85.
6	王海峰，沈鑫华.信噪比灰色关联分析在注塑工艺参数优化中的应用［J］.塑料工业，2019，47（4）：69-72.
	WANG H F， SHEN X H.Application of grey correlation analysis of SNR in injection molding process parameter optimization［J］.Plastics Industry，2019，47（4）：69-72.
7	胡钢，徐翔，张维明，等.基于主成分分析的网络节点重要性指标贡献评价［J］.电子学报，2019，47（2）：358-365.
	HU G， XU X， ZHANG W M，et al.Contribution analysis for assessing node importance indices with principal component analysis［J］.Acta Electronica Sinica，2019，47（2）：358-365.
8	PLANQUE B， ARNEBERG P. Principal component analy⁃ses for integrated ecosystem assessments may primarily ref⁃lect methodological artefacts［J］.ICES Journal of Marine Science，2018，75（3）：1 021-1 028.

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