Simulation and Analysis of Optimization Process Parameters for Multi-Cavity Injection Molding Parts Warpage by Genetic Algorithm Method

2011 ◽  
Vol 142 ◽  
pp. 54-57 ◽  
Author(s):  
Wen Jong Chen
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Array ◽  
Optimum Process ◽  
Process Conditions ◽  
Genetic Algorithm Method ◽  
Warpage Deformation

This study presents the warpage analysis for products through the combination of the genetic algorithm (GA) method and finite element method (FEM) in multi-cavity injection molding parts; it simulated and analyzed the warpage through controlling the conditions of various parameters in primary processes – filling, packing, and cooling. After 50 iterations of calculations. These results demonstrate that the maximum warpage of the products was 0.5052 mm for multi-cavity injection molding parts. In comparison with the orthogonal array, the maximum warpage could be reduced by approximately 7.24%. It is shown that the GA method can obtain the optimum process conditions for warpage deformation in multi-cavity injection molding parts.

Design of Optimization Parameters with Hybrid Genetic Algorithm Method in Multi-Cavity Injection Molding Process

2012 ◽  
Vol 463-464 ◽  
pp. 587-591 ◽  
Author(s):  
Wen Jong Chen ◽  
Jia Ru Lin
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Hybrid Genetic Algorithm ◽  
Injection Molding Process ◽  
Process Conditions ◽  
Molding Process ◽  
Optimization Parameters ◽  
Genetic Algorithm Method ◽  
Artificial Neural Network Ann ◽  
Cae Simulation

This paper combines an artificial neural network (ANN) with a traditional genetic algorithm (GA) method, called hybrid genetic algorithm (HGA), to analyze the warpage of multi-cavity plastic injection molding parts. Simulation results indicate that the minimum and the maximum warpage of the hybrid genetic algorithm (HGA) method were lower than that of the traditional GA method and CAE simulation. These results reveal that, when HGA is applied to multi-cavity plastic warpage analysis, the optimal process conditions are significantly better than those using the traditional GA method or CAE simulation.

scholarly journals Optimization of injection molding process parameters based on GA-ELM-GA

2022 ◽  
Vol 355 ◽  
pp. 01029
Author(s):  
Yi Mei ◽  
Maoyuan Xue
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Optimization Method ◽  
Injection Molding Process ◽  
Optimal Process ◽  
Range Analysis ◽  
Molding Process ◽  
Warpage Deformation

The most common optimization method for the optimization of injection mold process parameters is range analysis, but there is often a nonlinear coupling relationship between injection molding process parameters and quality indicators. Therefore, it is difficult to find the optimal process combination in range analysis. In this article, a genetic algorithm optimized extreme learning machine network model (GA-ELM) combined with genetic algorithm (GA) was proposed to optimize the process parameters of the injection mold. Take the injection molding process parameter optimization of an electrical appliance buckle cover shell as an example. In order to find the process parameters corresponding to the minimum warpage deformation, an orthogonal experiment was designed and the results of the orthogonal experiment were analyzed. Then, the corresponding optimal process combination and the degree of influence of process parameters on the warpage deformation were obtained. At the same time, the extreme learning machine network model (GA-ELM) optimized by the genetic algorithm was used to predict the warpage deformation of the plastic part. The trained GA-ELM model can map non-linear coupling relationship between the five process parameters and the warpage deformation well. And the optimal process parameters in the trained GA-ELM network model was searched by the powerful optimization ability of genetic algorithm. Generally speaking, the warpage deformation after optimization by range analysis is reduced by 6.7% compared with the minimum warpage after optimization by orthogonal experiment. But compared to the minimum warpage deformation after orthogonal experiment optimization, the warpage deformation after GAELM-GA optimization is reduced by 22%, which is better than that of the range analysis, thus verifying the feasibility and the optimization of the optimization method. This optimization method provides a certain theoretical reference and technical support for the field involving the optimization of process parameters.

scholarly journals Prediction and optimization of thinning in automotive sealing cover using Genetic Algorithm

2015 ◽  
Vol 3 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Ganesh M. Kakandikar ◽  
Vilas M. Nandedkar
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Process Parameters ◽  
Deep Drawing ◽  
Drawing Process ◽  
The Finite Element Method ◽  
Deep Drawing Process ◽  
Compressive Stresses ◽  
Element Method

Abstract Deep drawing is a forming process in which a blank of sheet metal is radially drawn into a forming die by the mechanical action of a punch and converted to required shape. Deep drawing involves complex material flow conditions and force distributions. Radial drawing stresses and tangential compressive stresses are induced in flange region due to the material retention property. These compressive stresses result in wrinkling phenomenon in flange region. Normally blank holder is applied for restricting wrinkles. Tensile stresses in radial direction initiate thinning in the wall region of cup. The thinning results into cracking or fracture. The finite element method is widely applied worldwide to simulate the deep drawing process. For real-life simulations of deep drawing process an accurate numerical model, as well as an accurate description of material behavior and contact conditions, is necessary. The finite element method is a powerful tool to predict material thinning deformations before prototypes are made. The proposed innovative methodology combines two techniques for prediction and optimization of thinning in automotive sealing cover. Taguchi design of experiments and analysis of variance has been applied to analyze the influencing process parameters on Thinning. Mathematical relations have been developed to correlate input process parameters and Thinning. Optimization problem has been formulated for thinning and Genetic Algorithm has been applied for optimization. Experimental validation of results proves the applicability of newly proposed approach. The optimized component when manufactured is observed to be safe, no thinning or fracture is observed.

Optimization of Process Parameters for Injection Molding Based on Taguchi Technique

2012 ◽  
Vol 538-541 ◽  
pp. 1170-1174
Author(s):  
Shi Jun Fu
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Design ◽  
Mold Temperature ◽  
Optimum Process ◽  
Process Conditions ◽  
Injection Time ◽  
Melt Temperature ◽  
Injection Process ◽  
Packing Pressure

In this paper, Taguchi and CAE technique are combined to study the influence of process conditions on the warpage of injection molding parts through twice orthogonal design experiments, and the injection process parameters are optimized according to the warpage. For the parameters selected, melt temperature and packing pressure have effects on the warpage of injection molding parts are highly significant, injection time is significant, other parameters have little effects. Within the range of experiments, the warpage decreased with the rise of the melt temperature and packing pressure. At last, the optimum process parameters of injection are that the mold temperature is 60°C, packing time is 10s, melt temperature is240°C, packing pressure is 115MPa and injection time is 0.4s.

Three-Dimensional Simulation of Co-Injection Molding

2001 ◽  
Author(s):  
Florin Ilinca ◽  
Jean-François Hétu
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Injection Molding ◽  
Three Dimensional ◽  
Mold Temperature ◽  
Dimensional Finite Element ◽  
Dimensional Simulation ◽  
Energy Equations ◽  
Three Dimensional Finite Element ◽  
Core Materials

Abstract This paper presents simulations of co-injection molding problems computed by a three-dimensional finite element method. The polymer melts behave as generalized Newtonian fluids and non-isothermal effects are taken into account. In addition to the momentum, mass and energy equations, we solve two transport equations tracking the polymer/air and skin/core polymers interfaces. Solutions are shown for a center gated rectangular plate. The effect of varying the melt/mold temperature and the ratio between the skin and core materials is investigated. The solution obtained for the same skin and core materials is compared with those in which viscosities of core and skin materials are different. Finally, the solution for the co-injection of a C-shaped plate is presented.

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