OPTIMIZATION OF INJECTION MOLDING QUALITY BASED ON BP NEURAL NETWORK AND PSO
Keywords:
orthogonal test, entropy weight, neural network, particle swarm optimization
Abstract
An electronic product shell is prone to uneven shrinkage, warpage and sink marks, resulting in a large number of unqualified products and increased costs. Therefore, volumetric shrinkage, warpage deformation and sink mark index are selected as optimization goals. Based on the orthogonal test and entropy weight method, a multi-objective optimization was transformed into a comprehensive evaluation optimization. A BP neural network combined with a particle swarm optimization algorithm was used to obtain the optimal combination of process parameters, simulated by Moldflow to reduce volumetric shrinkage to 3.46 %, warpage deformation to 2.538 mm, and the sink mark index to 1.87 % so as to improve the injection molding quality of the plastic parts and meet the requirements of qualified parts. The combination of the BP neural network and particle swarm optimization algorithm can prevent the defects such as large shrinkage, warpage and sink marks
References
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[3]X. F. Li, H. B. Liu, Optimization of Injection Molding Process Parameters Based on Taguchi Design of Experiment, Applied Mechanics & Materials, 233 (2012) 11, 335-338, doi:10.4028/www.scientific.net/AMM.233.335
[4]F. Wang, Z. L. Chen, Research on Process Optimization of Thin-Wall Plastics Injection Based on Moldflow and Orthogonal Experiment, Advanced Materials Research, 399-401 (2012), 1646-1649, doi:10.4028/www.scientific.net/AMR.399-401.1646
[5]G. Xu, Z. T. Yang, Multiobjective optimization of process parameters for plastic injection molding via soft computing and grey correlation analysis, The International Journal of Advanced Manufacturing Technology, 78 (2015) 1-4, 525-536, doi:10.1007/s00170-014-6643-4
[6]A. Sadeghi, A. Babakhani, S. M. Zebarjad, H. Mostajabodaveh, Use of grey relational analysis for multi-objective optimisation of NiTiCu shape memory alloy produced by powder metallurgy process, Journal of Intelligent Material Systems and Structures, 25 (2014) 16, 2093-2101, doi:10.1177/1045389X13517312
[7]S. Q. Sun, S. K. Zhang, M. X. Huang, Optimization of injection molding process parameters basedon signal-to-noise ratio and gray relational grade, Journal of Plasticity Engineering,23 (2016) 1, 141-145, doi:10.3969/j.issn.1007-2012.2016.01.026
[8]H. G. Zhang, Z. Y. Zhang, F. P. Wan, Y. Y. Liu, Q. X. Hu, Multi-objective optimization for dpvc process based on entropy-weight and rsm, Computer Integrated Manufacturing Systems, 20 (2014) 8, 1887-1895, doi:10.13196/j.cims.2014.08.zhangzhuangya.1887.9.20140811
[9]X. Y. Lu, L. X. Fan, Y. Y. Ding, Multi-Objective Optimization of FDM Molding Process Parameters Combined With Entropy Weight TOPSIS, Mechanical Science and Technology, 4 (2017) 11 , 1715-1721, doi:10.13433/j.cnki.1003-8728.2017.1113
[10]T. Peng, H. Jun, W.D. Shi, Optimization of a Centrifugal Pump Used as a Turbine Impeller By Means of an Orthogonal Test Approach, FDMP:Fluid Dynamics & Materials Processing, 15 (2019) 2, 139-151, doi:10.32604/fdmp.2019.05216
[11] H. Oktem, T. Erzurumlu, I. Uzman, Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part, Materials & design, 28 (2007) 4, 1271-1278, doi:https://doi.org/10.1016/j.matdes.2005.12.013
[12] M. Cong, S. Zhang, D. Sun, K. Zhou, Optimization of Preparation of Foamed Concrete Based on Orthogonal Experiment and Range Analysis, Frontiers in Materials, 8 (2021), 488, doi:10.3389/fmats.2021.778173
[13] K. Q. Zhong, Y. Xiao, X. Lu, J. Deng, Orthogonal Experimental Design and Range Analysis of TPCT Extraction of Coal Fire Heat, China Safety Science Journal, 31 (2021) 09, 135-141, doi:10.16265/j.cnki.issn1003-3033.2021.09.019
[14] Z. H. Zou, Y. Yi, J. N. Sun, Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment, Journal Of Environmental Sciences, 18 (2006) 5, 1020-1023, doi:10.3321/j.issn:1001-0742.2006.05.032
[15] C. Qiao, Y. Wang, C. H. Li, B. Q. Yan, Application of Extension Theory Based on Improved Entropy Weight Method to Rock Slope Analysis in Cold Regions, Geotechnical and geological engineering, 39 (2021) 6, 4315-4327, doi:10.1007/s10706-021-01760-9.
[16] Y. L. Cao, X. Y. Fan, Y. H. Guo, S. Li, H. Y. Huang, Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods, Journal of Polymer Engineering, 40 (2020) 4, 360-371, doi:10.1515/polyeng-2019-0326
[17] Y. Kinouchi, G. Ohara, H. Nagashino, T. Soga, F. Shichijo, K. Matsumoto, Dipole source localization of MEG by BP neural networks, Brain topography, 8 (1996) 3, 317-21, doi:10.1007/BF01184791
[18] Z. Y. Song, S. M. Liu, X. X. Wang, Z. X. Hu, Optimization and prediction of volume shrinkage and warpage of injection-molded thin-walled parts based on neural network, The International Journal of Advanced Manufacturing Technology, 109 (2020) 3, 755-769, doi:10.1007/s00170-020-05558-6
[19]M. L. Cong, S. S. Zhang, D. D. Sun, K. P. Zhou, Optimization of Injection Molding Process of Bearing Stand Based on BP Network Method, Transactions of Nanjing University of Aeronautics and Astronautic, 31(2014)2, 180-185, doi:10.3969/j.issn.1005-1120.2014.02.014
[20] K. H. Fuh, S. B. Wang, Force modeling and forecasting in creep feed grinding using improved BP neural network, International Journal of Machine Tools and Manufacture, 37 (1997) 8, 1167-1178, doi:10.1016/S0890-6955(96)00012-0
[21] D. Kramar, D. Cica, Predictive model and optimization of processing parameters for plastic injection moulding, Materiali in tehnologije, 51(2017)4, 597-602, doi:10.17222/mit.2016.129
[22] F. Yin, H. J. Mao, L. Hua, A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters, Materials & Design, 32 (2011) 6, 3457-3464, doi:10.1016/j.matdes.2011.01.058
Published
2022-10-04
How to Cite
1.
Lin F, Duan J, Lu Q, Li X. OPTIMIZATION OF INJECTION MOLDING QUALITY BASED ON BP NEURAL NETWORK AND PSO. MatTech [Internet]. 2022Oct.4 [cited 2025Nov.18];56(5):491–497. Available from: https://mater-tehnol.si/index.php/MatTech/article/view/516
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