Multi-objective optimization of the GFRP injection molding process parameters by using GA-ELM, MOFA and GRA-TOPSIS

2021 ◽  
Author(s):  
Xin Liu ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Yanli Cao ◽  
Chunxiao Li
Keyword(s):  
Injection Molding ◽  
Extreme Learning Machine ◽  
Prediction Models ◽  
Volume Shrinkage ◽  
Injection Molding Process ◽  
Pareto Optimal ◽  
Molding Process ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Learning Machine

Due to the influence of injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by the glass fiber-reinforced plastic (GFRP) injection molding. In order to minimize the two defects, the extreme learning machine optimized by genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA) and a multi-objective decision-making method called GRA-TOPSIS are implemented in this study. All experiments based on Latin hypercubic sampling (LHS) are conducted by Moldflow software to obtain results of warpage and volume shrinkage. The prediction accuracy of defect prediction models based on the extreme learning machine (ELM) and GA-ELM algorithm is compared. The results show that GA-ELM models can better predict defect values. Finally, MOFA is utilized to find the Pareto optimal front, and the GRA-TOPSIS method is used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage are effectively reduced by 12.25% and 6.11% compared with those before optimization, respectively, which indicates the effectiveness and reliability of the optimization method.

Robust Design in Injection Molding Processing Based on Multi-Objective Ant Colonies Algorithm with Crossover and Variation

2011 ◽  
Vol 88-89 ◽  
pp. 279-284
Author(s):  
Feng Li Huang ◽  
Mei Peng Zhong ◽  
Jin Mei Gu ◽  
G.W. Liu
Keyword(s):  
Standard Deviation ◽  
Injection Molding ◽  
Robust Design ◽  
Injection Molding Process ◽  
Molding Process ◽  
Ant Colonies ◽  
Multi Objective ◽  
Plastic Parts ◽  
Crossover And Mutation ◽  
Plastic Injection

Based on single objective robust design of injection molding process, a bi-objective robust design model based on mean and standard deviation of molding quality and a multi-objective ant colonies algorithm with crossover and mutation based on Pareto optimization are proposed. Aimed at the craft parameters of plastic injection for the top and down shell of remote controller, a model of bi-objective robust design based on mean and standard deviation of warpage quantity is established with an example. And the model is solved by multi-objective ant colonies algorithm of crossover and mutation. The result shows that partial performances of algorithm are superior to that of NSGAII. The actual plastic injection was done by means of the parameters which were gotten by multi-objective robust optimization. The quality of plastic parts was high, and the fluctuation was small.

Injection Molding Process Optimization of Multi-Objective Based on MUD-RBFNN-GA

2010 ◽  
Vol 37-38 ◽  
pp. 564-569
Author(s):  
Bao Shou Sun ◽  
Zhe Chen ◽  
Bo Qin Gu ◽  
Xiao Diao Huang
Keyword(s):  
Injection Molding ◽  
Process Optimization ◽  
Simulation Analysis ◽  
Weld Line ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Multi Objective ◽  
Uniform Designs ◽  
Moldflow Simulation

The optimization algorithm of MUD-RBFNN-GA was proposed in this article. An injection molding process optimization model of multi-factor and multi-objective was also researched. The multiple uniform designs of experiment was applied to optimize the processing parameters. During this process, the RBF neural network was established, where the melt temperature, mold temperature and packing pressure were taken as the inputs, and warpage, area of air-traps and weld-line length as the outputs, and the Moldflow simulation analysis was used to obtain the output values. By combining the algorithm with genetic algorithm and global optimization in the networks, we can get the optimal process parameters. The results show that the multi-objective optimization based on MUD-RBFNN-GA is practically applicable, and it can reduce the molding defects effectively.

Multi-objective Optimization of an Injection Molding Process

2016 ◽  
pp. 391-407
Author(s):  
Alejandro Alvarado-Iniesta ◽  
Jorge L. García-Alcaraz ◽  
Arturo Del Valle-Carrasco ◽  
Luis A. Pérez-Domínguez
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Multi Objective

Multi-Objective Optimization in Manufacturing Engineering for Slender Pen Rod Injection Molding Quality Based on Grey Correlation

2013 ◽  
Vol 345 ◽  
pp. 486-493
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Grey Relational Analysis ◽  
Injection Molding Process ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Melt Temperature ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper a new approach for the optimization of the multi-objective injection molding process based on the Taguchi robust design combined with the grey relational analysis has been studied. A grey relational grade obtained from the multi-objective grey relational analysis is used to solve the injection molding process with the multiple performance characteristics including volume shrinkage (R1) and axial deformation (R2), the injecting parameters, namely mold temperature, melt temperature, holding pressure and holding time are optimized. By orthogonal polar difference analysis and statistical analysis of variance (ANOVA) of grey relational grade, main factors influencing and the best process parameters were determined: A=50°C,B=250°C,C=30MPa,D=9s.Under the case of continuity factor, Fitting the response surface further the optimal combination of in continuous space r is identified: A=50.3°C,B=250°C,C=29MPa,D=8.3s. Experimental results have shown that the Taguchi combined with the grey relational analysis can avoid human evaluation of the multi-objective optimization, and Injection molding multi-objective optimization is implemented more objectively, and product performance in the process can be improved effectively through this approach.

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