Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO

Huan-Lao Liu; Zi-Lin Zhang; Pei-Ming Peng; Can Liu

doi:doi:10.11648/j.ijmsa.20221104.11

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Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO

Huan-Lao Liu, Zi-Lin Zhang, Pei-Ming Peng, Can Liu

Published in International Journal of Materials Science and Applications (Volume 11, Issue 4)

Received: 30 August 2022 Accepted: 15 September 2022 Published: 21 September 2022

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Abstract

Taking the amount of warpage deformation of automotive brake Plug-in as the research object, UG NX10.0 software was used to design the product model and Moldflow software was used for predictive analysis of the product model. The preliminary optimized parameters were obtained by using the response surface method - central composite experiment design (CCD) and combined with injection molding CAE technology. The mold temperature was 70°C, the melt temperature was 250°C, the holding pressure was 95 MPa, and the holding time was 10s. The warpage was 1.18 mm. Through the analysis of variance, the influence of four injection process parameters on product warpage was obtained, namely, holding pressure>holding time>melt temperature>mold temperature. Based on the fitted response surface algorithm model, the process parameters were optimized using particle swarm optimization (PSO) with the minimum warpage as the constraint condition, and the optimal combination of the optimized process parameters was obtained, that was, the mold temperature was 60°C, the melt temperature was 280°C, the holding pressure was 95 MPa and the holding time was 8.296 s. The minimum warpage was 1.057 mm. The optimization results showed that the minimum warpage was reduced by 10.85% compared with the initially optimized parameters, and the effectiveness of the proposed method was verified by using this parameter combination for actual injection production.

Published in	International Journal of Materials Science and Applications (Volume 11, Issue 4)
DOI	10.11648/j.ijmsa.20221104.11
Page(s)	84-94
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Automotive Brake Plug-in, Response Surface, Injection Molding Process Parameters, Warpage, Particle Swarm Optimization

References

[1]	SINGH G, PRADHAN M K, VERMA A. Multi response optimization of injection moulding process parameters to reduce cycle time and warpage [J]. Materials today: Proceedings, 2018, 5 (2).
[2]	Chen Y, Zhu J. Warpage analysis and optimization of thin-walled injection molding parts based on numerical simulation and orthogonal experiment [J]. IOP Conference Series: Materials Science and Engineering, 2019, 688 (3): 033027.
[3]	Wang G, Wang Y, Yang D. Study on Automotive Back Door Panel Injection Molding Process Simulation and Process Parameter Optimization [J]. Advances in Materials Science and Engineering, 2021, 2021: 9996423.
[4]	Rosli M U, Ahmad Termizi S N A, Khor C Y, et al. Simulation Based Optimization of Thin Wall Injection Molding Parameter Using Response Surface Methodology [J]. IOP Conference Series: Materials Science and Engineering, 2020, 864 (1): 012193.
[5]	HEIDARI B S, DAVACHI S M, MOGHADDAM A H, et al. Optimization simulated injection molding process for ultrahigh molecular weight polyethylene nanocomposite hip liner using response surface methodology and simulation of mechanical behavior [J]. Journal of the Mechanical Behavior of Biomedical Materials, 2018, 81: 95-105.
[6]	Rajalingam S, Vasant I, Khe C S, et al. Optimization of Injection Molding Process Parameters by Response Surface Methods [J]. Journal of Information Technology & Software Engineering, 2016, 6 (2).
[7]	Chen, Wu-Lin, Huang, et al. Optimization of plastic injection molding process by dual response surface method with non-linear programming [J]. Engineering Computations, 2010, 27 (8).
[8]	Yuan Z, Wang H, Wei X, et al. Multiobjective Optimization Method for Polymer Injection Molding Based on a Genetic Algorithm [J]. Advances in Polymer Technology, 2019, 2019: 1-17.
[9]	KITAYAMA S, YAMAZAKI Y, TAKANO M, et al. Numerical and experimental investigation of process parameters optimization in plastic injection molding using multi-criteria decision making [J]. Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies, 2018, 85: 95-105.
[10]	Yin F, Mao H, Hua L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters [J]. Materials & Design, 2011, 32 (6): 3457-3464.
[11]	Zhao J, Cheng G, Ruan S, et al. Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm [J]. The International Journal of Advanced Manufacturing Technology, 2015, 78 (9-12): 1813-1826.
[12]	Cao Y, Fan X, Guo Y, et al. Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods [J]. Journal of Polymer Engineering, 2020, 40 (4): 360-371.
[13]	Tsai K-M, Luo H-J. An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm [J]. Journal of Intelligent Manufacturing, 2017, 28 (2): 473-487.
[14]	Liu X, Fan X, Guo Y, et al. Multi-objective optimization of GFRP injection molding process parameters, using GA-ELM, MOFA, and GRA-TOPSIS [J]. Transactions of the Canadian Society for Mechanical Engineering, 2021, 46 (1): 37-49.
[15]	Li K, Yan S, Pan W, et al. Warpage optimization of fiber-reinforced composite injection molding by combining back propagation neural network and genetic algorithm [J]. International Journal of Advanced Manufacturing Technology, 2017, 90 (1-4): 963-970.
[16]	Sudsawat S, Sriseubsai W. Optimized plastic injection molding pr ocess and minimized the warpage and volume shrinkage by response surface methodology with genetic algorithm and firefly algorithm techniques [J]. Indian Journal of Engineering and Materials Sciences, 2017, 24 (3): 228-238.
[17]	FENG Q Q, ZHOU X H. Automated and robust multi-objective optimal design of thin-walled product injection process based on hybrid RBF-MOGA [J]. The International Journal of Advanced Manufacturing Technology, 2019, 101 (9/12): 2217-2231.
[18]	Oktem H, Shinde D. Determination of Optimal Process Parameters for Plastic Injection Molding of Polymer Materials Using Multi-Objective Optimization [J]. Journal of Materials Engineering and Performance, 2021, 30 (11): 8616-8632.
[19]	Rathore A, Sharma H. Review on inertia weight strategies for particle swarm optimization [C]. Proceedings of 6th International Conference on Soft Computing for Problem Solving, SocProS 2016, December 23, 2016 - December 24, 2016, 2017: 73-83.
[20]	SHI Y H, EBERHART R C. A modified particle swarm optimizer [C]. 1998 of the IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence. New Jersey: IEEE Press, 1998: 69-73.
[21]	SHI Y H, EBERHART R C. Empirical study of particle swarm optimization [J]. Congress on Evolutionary Computation, 1999, 3: 1945-1950.

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APA Style

Huan-Lao Liu, Zi-Lin Zhang, Pei-Ming Peng, Can Liu. (2022). Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO. International Journal of Materials Science and Applications, 11(4), 84-94. https://doi.org/10.11648/j.ijmsa.20221104.11

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ACS Style

Huan-Lao Liu; Zi-Lin Zhang; Pei-Ming Peng; Can Liu. Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO. Int. J. Mater. Sci. Appl. 2022, 11(4), 84-94. doi: 10.11648/j.ijmsa.20221104.11

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AMA Style

Huan-Lao Liu, Zi-Lin Zhang, Pei-Ming Peng, Can Liu. Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO. Int J Mater Sci Appl. 2022;11(4):84-94. doi: 10.11648/j.ijmsa.20221104.11

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@article{10.11648/j.ijmsa.20221104.11,
  author = {Huan-Lao Liu and Zi-Lin Zhang and Pei-Ming Peng and Can Liu},
  title = {Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO},
  journal = {International Journal of Materials Science and Applications},
  volume = {11},
  number = {4},
  pages = {84-94},
  doi = {10.11648/j.ijmsa.20221104.11},
  url = {https://doi.org/10.11648/j.ijmsa.20221104.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmsa.20221104.11},
  abstract = {Taking the amount of warpage deformation of automotive brake Plug-in as the research object, UG NX10.0 software was used to design the product model and Moldflow software was used for predictive analysis of the product model. The preliminary optimized parameters were obtained by using the response surface method - central composite experiment design (CCD) and combined with injection molding CAE technology. The mold temperature was 70°C, the melt temperature was 250°C, the holding pressure was 95 MPa, and the holding time was 10s. The warpage was 1.18 mm. Through the analysis of variance, the influence of four injection process parameters on product warpage was obtained, namely, holding pressure>holding time>melt temperature>mold temperature. Based on the fitted response surface algorithm model, the process parameters were optimized using particle swarm optimization (PSO) with the minimum warpage as the constraint condition, and the optimal combination of the optimized process parameters was obtained, that was, the mold temperature was 60°C, the melt temperature was 280°C, the holding pressure was 95 MPa and the holding time was 8.296 s. The minimum warpage was 1.057 mm. The optimization results showed that the minimum warpage was reduced by 10.85% compared with the initially optimized parameters, and the effectiveness of the proposed method was verified by using this parameter combination for actual injection production.},
 year = {2022}
}

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TY  - JOUR
T1  - Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO
AU  - Huan-Lao Liu
AU  - Zi-Lin Zhang
AU  - Pei-Ming Peng
AU  - Can Liu
Y1  - 2022/09/21
PY  - 2022
N1  - https://doi.org/10.11648/j.ijmsa.20221104.11
DO  - 10.11648/j.ijmsa.20221104.11
T2  - International Journal of Materials Science and Applications
JF  - International Journal of Materials Science and Applications
JO  - International Journal of Materials Science and Applications
SP  - 84
EP  - 94
PB  - Science Publishing Group
SN  - 2327-2643
UR  - https://doi.org/10.11648/j.ijmsa.20221104.11
AB  - Taking the amount of warpage deformation of automotive brake Plug-in as the research object, UG NX10.0 software was used to design the product model and Moldflow software was used for predictive analysis of the product model. The preliminary optimized parameters were obtained by using the response surface method - central composite experiment design (CCD) and combined with injection molding CAE technology. The mold temperature was 70°C, the melt temperature was 250°C, the holding pressure was 95 MPa, and the holding time was 10s. The warpage was 1.18 mm. Through the analysis of variance, the influence of four injection process parameters on product warpage was obtained, namely, holding pressure>holding time>melt temperature>mold temperature. Based on the fitted response surface algorithm model, the process parameters were optimized using particle swarm optimization (PSO) with the minimum warpage as the constraint condition, and the optimal combination of the optimized process parameters was obtained, that was, the mold temperature was 60°C, the melt temperature was 280°C, the holding pressure was 95 MPa and the holding time was 8.296 s. The minimum warpage was 1.057 mm. The optimization results showed that the minimum warpage was reduced by 10.85% compared with the initially optimized parameters, and the effectiveness of the proposed method was verified by using this parameter combination for actual injection production.
VL  - 11
IS  - 4
ER  -

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Author Information

Huan-Lao Liu

School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang, PR China
Zi-Lin Zhang

School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang, PR China
Pei-Ming Peng

School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang, PR China
Can Liu

School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang, PR China

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Plain Text BibTeX RIS

APA Style

Huan-Lao Liu, Zi-Lin Zhang, Pei-Ming Peng, Can Liu. (2022). Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO. International Journal of Materials Science and Applications, 11(4), 84-94. https://doi.org/10.11648/j.ijmsa.20221104.11

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ACS Style

Huan-Lao Liu; Zi-Lin Zhang; Pei-Ming Peng; Can Liu. Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO. Int. J. Mater. Sci. Appl. 2022, 11(4), 84-94. doi: 10.11648/j.ijmsa.20221104.11

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AMA Style

Huan-Lao Liu, Zi-Lin Zhang, Pei-Ming Peng, Can Liu. Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO. Int J Mater Sci Appl. 2022;11(4):84-94. doi: 10.11648/j.ijmsa.20221104.11

Copy | Download

@article{10.11648/j.ijmsa.20221104.11,
  author = {Huan-Lao Liu and Zi-Lin Zhang and Pei-Ming Peng and Can Liu},
  title = {Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO},
  journal = {International Journal of Materials Science and Applications},
  volume = {11},
  number = {4},
  pages = {84-94},
  doi = {10.11648/j.ijmsa.20221104.11},
  url = {https://doi.org/10.11648/j.ijmsa.20221104.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmsa.20221104.11},
  abstract = {Taking the amount of warpage deformation of automotive brake Plug-in as the research object, UG NX10.0 software was used to design the product model and Moldflow software was used for predictive analysis of the product model. The preliminary optimized parameters were obtained by using the response surface method - central composite experiment design (CCD) and combined with injection molding CAE technology. The mold temperature was 70°C, the melt temperature was 250°C, the holding pressure was 95 MPa, and the holding time was 10s. The warpage was 1.18 mm. Through the analysis of variance, the influence of four injection process parameters on product warpage was obtained, namely, holding pressure>holding time>melt temperature>mold temperature. Based on the fitted response surface algorithm model, the process parameters were optimized using particle swarm optimization (PSO) with the minimum warpage as the constraint condition, and the optimal combination of the optimized process parameters was obtained, that was, the mold temperature was 60°C, the melt temperature was 280°C, the holding pressure was 95 MPa and the holding time was 8.296 s. The minimum warpage was 1.057 mm. The optimization results showed that the minimum warpage was reduced by 10.85% compared with the initially optimized parameters, and the effectiveness of the proposed method was verified by using this parameter combination for actual injection production.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Optimization of Injection Molding Process Parameters for Automotive Brake Plug-in Based on CCD and PSO
AU  - Huan-Lao Liu
AU  - Zi-Lin Zhang
AU  - Pei-Ming Peng
AU  - Can Liu
Y1  - 2022/09/21
PY  - 2022
N1  - https://doi.org/10.11648/j.ijmsa.20221104.11
DO  - 10.11648/j.ijmsa.20221104.11
T2  - International Journal of Materials Science and Applications
JF  - International Journal of Materials Science and Applications
JO  - International Journal of Materials Science and Applications
SP  - 84
EP  - 94
PB  - Science Publishing Group
SN  - 2327-2643
UR  - https://doi.org/10.11648/j.ijmsa.20221104.11
AB  - Taking the amount of warpage deformation of automotive brake Plug-in as the research object, UG NX10.0 software was used to design the product model and Moldflow software was used for predictive analysis of the product model. The preliminary optimized parameters were obtained by using the response surface method - central composite experiment design (CCD) and combined with injection molding CAE technology. The mold temperature was 70°C, the melt temperature was 250°C, the holding pressure was 95 MPa, and the holding time was 10s. The warpage was 1.18 mm. Through the analysis of variance, the influence of four injection process parameters on product warpage was obtained, namely, holding pressure>holding time>melt temperature>mold temperature. Based on the fitted response surface algorithm model, the process parameters were optimized using particle swarm optimization (PSO) with the minimum warpage as the constraint condition, and the optimal combination of the optimized process parameters was obtained, that was, the mold temperature was 60°C, the melt temperature was 280°C, the holding pressure was 95 MPa and the holding time was 8.296 s. The minimum warpage was 1.057 mm. The optimization results showed that the minimum warpage was reduced by 10.85% compared with the initially optimized parameters, and the effectiveness of the proposed method was verified by using this parameter combination for actual injection production.
VL  - 11
IS  - 4
ER  -

Copy | Download