Skip to main content

Advertisement

SpringerLink
Log in

Decision-making for structural parameters of injection mold gating system based on agent model and intelligent algorithm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

A Correction to this article was published on 06 February 2022

This article has been updated

Abstract

For the flat plastic parts with the size range of 200 to 1300 mm, the structural parameters decision model of injection mold gating system is established in this paper. Firstly, the relationship between the warpage and the structural parameters of gating system is fitted by Kriging model, and the minimum warpage is optimized by genetic algorithm (GA) to obtain the best structural parameters. Secondly, the best structural parameters of gating system corresponding to 16 groups of plastic parts are taken as samples, and the relationship between the structural parameters of gating system and the size parameters of plastic parts is established by Kriging model. In addition, K-means is used to reduce the number of the structural parameters of gating system from 5 to 2, in order to improve the accuracy of decision model. Finally, the size parameters of plastic parts are input to the decision model to obtain the structural parameters of gating system. Through the verification of mold flow analysis experiment, the structural parameters of gating system obtained by this method meet the design requirements and can effectively shorten the structural design cycle of injection mold.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Availability of data and materials

All the data presented and/or analyzed in this study are available upon request to the corresponding author.

Change history

References

  1. Cai K, Wang Y, Lu S (2019) Research on optimization design of injection mold for automobile filter shell model based on BP neural network. IOP Conf Ser Mate Sci Eng 612:032014. https://doi.org/10.1088/1757-899X/612/3/032014

    Article  Google Scholar 

  2. Heidari BS, Moghaddam AH, Davachi SM, Khamani S, Alihosseini A (2019) Optimization of process parameters in plastic injection molding for minimizing the volumetric shrinkage and warpage using radial basis function (RBF) coupled with the k-fold cross validation technique. J Polym Eng 39(5):481–492. https://doi.org/10.1515/polyeng-2018-0359

    Article  Google Scholar 

  3. Kumar S, Singh AK (2019) Warpage and shrinkage analysis and optimization of rapid tooling molded thin wall component using modified particle swarm algorithm. J Adv Manuf Syst. https://doi.org/10.1142/S0219686719500045

    Article  Google Scholar 

  4. Feng Q, Liu L, Zhou X (2020) Automated multi-objective optimization for thin-walled plastic products using Taguchi, Anova, and hybrid Ann-moga. Int J Adv Manuf Technol 106(1–2):559–575. https://doi.org/10.1007/s00170-019-04488-2

    Article  Google Scholar 

  5. Radhwan H, Nasir SM, Rashidi MM, Kamarudin K (2019) Optimization parameters to reduce the warpage defect of plastic injection molding process for a thin-shell par using design of experiment. Joint Conf Green Eng Technol Appl Comput 551:012027. https://doi.org/10.1088/1757-899X/551/1/012027

    Article  Google Scholar 

  6. Sun Q, Wang M, Pan R, Xin W, Lv H (2013) Analysis of warpage and optimization of parameter for thin-wall plastic part Moldflow-based software. Appl Mech Mater 377:133–137. https://doi.org/10.4028/www.scientific.net/AMM.377.133

    Article  Google Scholar 

  7. Yang Y, Wang M (2018) Using Moldflow to optimize the pouring system of bucket injection mould. IOP Conf Ser Mater Sci Eng 394:032065. https://doi.org/10.1088/1757-899X/394/3/032065

    Article  Google Scholar 

  8. Wang Z, Miao L, Meng L (2013) Optimal design of plastic injection mould gating system. China Plast Ind 189–193:2371–2375. https://doi.org/10.4028/www.scientific.net/AMR.189-193.2371

    Article  Google Scholar 

  9. Liu X, Hu Y, Huang W (2011) Optimum design of plastic injection mould gate based on Moldflow. China Plast Ind 239–242:2541–2544. https://doi.org/10.4028/www.scientific.net/AMR.239-242.2541

    Article  Google Scholar 

  10. Cheng X, Liu S, Zheng X, Seng N (2012) Effect of gate number on the warpage in injection molding. Springer, Berlin Heidelberg 0:241–246. https://doi.org/10.1007/978-3-642-27314-8_34

    Article  Google Scholar 

  11. Chen D, Pan J, Gao J, Chen W (2013) Design and surface roughness analysis of thin-sheet plastic injection forming. Lect Notes Electr Eng 234:743–753. https://doi.org/10.1007/978-1-4614-6747-2_86

    Article  Google Scholar 

  12. Ruan X, Zhou Q, Shu L, Hu J, Cao L (2018) Accurate prediction of the weld bead characteristic in laser keyhole welding based on the stochastic kriging model. Metals Open Access Metall J 8(7):486. https://doi.org/10.3390/met8070486

    Article  Google Scholar 

  13. Nguyen TT, Duong QD (2019) Optimization of WEDM process of mould material using kriging model to improve technological performances. Sādhanā 44(6):1–16. https://doi.org/10.1007/s12046-019-1133-x

    Article  Google Scholar 

  14. Lu P, Xu Z, Chen Y, Zhou Y (2020) Prediction method of bridge static load test results based on kriging model. Eng Struct 214:110641. https://doi.org/10.1016/j.engstruct.2020.110641

    Article  Google Scholar 

  15. Wang H, Wang L, Yang L, Shi X, Wen Z, Dong X (2021) Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network. Comput Biol Med 134:104494. https://doi.org/10.1016/j.compbiomed.2021.104494

    Article  Google Scholar 

  16. Ma Y, Li L, Yin Z, Chai A, Li M, Bi Z (2021) Research and application of network status prediction based on BP neural network for intelligent production line. Procedia Comput Sci 183(20):189–196. https://doi.org/10.1016/j.procs.2021.02.049

    Article  Google Scholar 

  17. Han Z, Hong L, Meng J, Li Y, Gao Q (2020) Temperature drift modeling and compensation of capacitive accelerometer based on AGA-BP neural network. Measurement 164:108019. https://doi.org/10.1016/j.measurement.2020.108019

    Article  Google Scholar 

  18. Wu L, Yang Y, Maheshwari M (2020) Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network. Mar Struct 72:102762. https://doi.org/10.1016/j.marstruc.2020.102762

    Article  Google Scholar 

  19. Deng Y, Xiao H, Xu J, Wang H (2019) Prediction model of PSO-BP neural network on coliform amount in special food. Saudi J Biol Sci 26(6):1154–1160. https://doi.org/10.1016/j.sjbs.2019.06.016

    Article  Google Scholar 

  20. Zeraati M, Chen TC, Ebri M, Chauhan NPS, Sargazi G (2021) Length prediction of silicon nanowires (SiNWs) prepared by the mace method using the ANN-COA-PSO algorithm for high supercapacitor applications. J Phys Chem Solids 156:110146. https://doi.org/10.1016/j.jpcs.2021.110146

    Article  Google Scholar 

  21. Pant P, Chatterjee D (2020) Prediction of clad characteristics using ANN and combined PSO-ANN algorithms in laser metal deposition process. Surf Interfaces 21:100699. https://doi.org/10.1016/j.surfin.2020.100699

    Article  Google Scholar 

  22. Feng Q, Zhou X (2019) Automated and robust multi-objective optimal design of thin-walled product injection process based on hybrid RBF-MOGA. Int J Adv Manuf Technol 101:2217–2231. https://doi.org/10.1007/s00170-018-3084-5

    Article  Google Scholar 

  23. Yang J, Yu S, Yu M (2020) Study of residual wall thickness and multi objective optimization for process parameters of water-assisted injection molding. Adv Polym Technol 2020:1–11. https://doi.org/10.1155/2020/3481752

    Article  Google Scholar 

  24. Hashimoto S, Kitayama S, Takano M, Kubo Y, Aibi S (2020) Simultaneous optimization of variable injection velocity profile and process parameters in plastic injection molding for minimizing weld line and cycle time. J Adv Mech Des Syst Manuf 14(3):0029. https://doi.org/10.1299/jamdsm.2020jamdsm0029

    Article  Google Scholar 

Download references

Funding

This work supported by the Beijing Science and Technology Project (No. Z201100006720008).

Author information

Authors and Affiliations

  1. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, 100124, China

    Hongyan Chu, Zhijian Liu, Caixia Zhang, Zhifeng Liu, Yongsheng Zhao & Congbin Yang

  2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China

    Hongyan Chu, Zhijian Liu, Caixia Zhang, Zhifeng Liu, Yongsheng Zhao & Congbin Yang

Authors
  1. Hongyan Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhijian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caixia Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhifeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongsheng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Congbin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors confirm that the information provided is accurate. In addition, the substantial contribution to the depicted research by all authors is confirmed.

Corresponding author

Correspondence to Caixia Zhang.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: Corresponding author should be "Caixia Zhang".

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chu, H., Liu, Z., Zhang, C. et al. Decision-making for structural parameters of injection mold gating system based on agent model and intelligent algorithm. Int J Adv Manuf Technol 119, 7599–7614 (2022). https://doi.org/10.1007/s00170-022-08756-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-08756-6

Keywords

Search

Navigation