scholarly journals Optimization of Process Parameters in Injection Molding of Mounting Panel of Automobile Air Conditioner Housing Based on Moldflow and Taguchi Orthogonal Experiment

2017 ◽  
Author(s):  
Jing Li ◽  
Yang Xu ◽  
Cuihong Han ◽  
Xiaoru Guo ◽  
Chunxia Hu ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Air Conditioner ◽  
Optimization Of Process Parameters ◽  
Automobile Air Conditioner

Thin-Wall Plastic Parts’ Warpage Analysis Based on Taguchi Method

2011 ◽  
Vol 80-81 ◽  
pp. 375-378
Author(s):  
Jian Zhong Chu ◽  
Rong Song
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Test ◽  
Thin Wall ◽  
Engineering Technology ◽  
Important Indicator ◽  
Leading Role ◽  
Optimization Of Process Parameters ◽  
Wall Injection ◽  
Plastic Parts

Warping deformation is an important indicator to evaluate the performance of thin-wall Plastic. In this paper, using the CAE（computer aided engineering）technology and DOE（Design of experiment）in the thin-wall injection molding field, take a box-shaped thin-wall plastic parts for example, using the moldflow software to simulation analyze of the process parameters of injection molding. By analyzing the causes of plastic parts’ warpage, learn the holding pressure is a leading role in warping. Optimization of process parameters under the guidance of the orthogonal test, it can reduce the warpage of plastic parts effectively.

scholarly journals Optimization of Process Parameters of Stamping Forming of the Automotive Lower Floor Board

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoying Ma ◽  
Binbing Huang
Keyword(s):  
Process Parameters ◽  
Production Efficiency ◽  
Simulation Analysis ◽  
Orthogonal Experiment ◽  
Production Costs ◽  
Main Process ◽  
Forming Process ◽  
Optimization Of Process Parameters ◽  
Automobile Panel ◽  
Thinning Rate

There are many process parameters which have great effect on the forming quality of parts during automobile panel stamping forming process. This paper took automotive lower floor board as the research object; the forming process was analyzed by finite element simulation using Dynaform. The influences of four main process parameters including BHF (blank holder force), die corner radius, friction coefficient, and die clearance on the maximum thinning rate and the maximum thickening rate were researched based on orthogonal experiment. The results show that the influences of each value of various factors on the target are not identical. On this basis, the optimization of the four parameters was carried out, and the high quality product was obtained and the maximum thinning rate and maximum thickening rate were effectively controlled. The results also show that the simulation analysis provides the basis for the optimization of the forming process parameters, and it can greatly shorten the die manufacturing cycles, reduce the production costs, and improve the production efficiency.

Application of Optimization Algorithms in Injection Molding Process Parameters Optimization

2011 ◽  
Vol 704-705 ◽  
pp. 183-190
Author(s):  
Ze Hao Hu ◽  
Wei Wei ◽  
Juan Liu ◽  
Kun Liu
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Design Method ◽  
Orthogonal Experiment ◽  
Parameters Optimization ◽  
Injection Molding Process ◽  
Molding Process ◽  
Bp Artificial Neural Network ◽  
Agent Model ◽  
Set Up

In this paper, the numerical simulation and calculation of injection molding process are taken in the Moldflow software. The BP artificial neural network combining with the orthogonal experiment design method is used to set up the injection molding process agent model, genetic algorithms are applied to realize global optimization, finally, the optimal combination of process parameters of each quality indicators is obtained.

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 Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Junhui Liu ◽  
Xindu Chen ◽  
Zeqin Lin ◽  
Shipu Diao
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Fitness Function ◽  
Injection Molding Process ◽  
Optical Performance ◽  
Surface Waviness ◽  
Molding Process ◽  
Nsga Ii ◽  
Optical Lens

Injection molding process parameters (IMPP) have a significant effect on the optical performance and surface waviness of precision plastic optical lens. This paper presents a set of procedures for the optimization of IMPP, with haze ratio (HR) reflecting the optical performance and peak-to-valley 20 (PV20) reflecting the surface waviness as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the results were analyzed by ANOVA to screen out the IMPP having a significant effect on the objectives. Then, the 34 full-factor experiment was conducted on the key IMPP, and the experimental results were used as the training and testing samples. The BPNN algorithm and the M-SVR algorithm were applied to establish the mapping relationships between the IMPP and objectives. Finally, the multiple-objective optimization was performed by applying the nondominated sorting genetic algorithm (NSGA-II), with the built M-SVR models as the fitness function of the objectives, to obtain a Pareto-optimal set, which improved the quality of plastic optical lens comprehensively. Through the experimental verification on the optimization results, the mean prediction error (MPE) of HR and PV20 is 7.16% and 9.78%, respectively, indicating that the optimization method has high accuracy.

Simulation Analysis of Residual Stress of the Plastic Gear Based on Moldflow

2012 ◽  
Vol 501 ◽  
pp. 339-343 ◽  
Author(s):  
Meng Li ◽  
Hui Min Zhang ◽  
Yong Nie
Keyword(s):  
Residual Stress ◽  
Injection Molding ◽  
Process Parameters ◽  
Simulation Analysis ◽  
Gear Tooth ◽  
Orthogonal Experiment ◽  
Injection Molding Process ◽  
Tooth Root ◽  
Molding Process ◽  
Melt Temperature

Through the orthogonal experiment with Moldflow software, numerical simulation was conducted in different injection molding process parameters. The influence on the plastic gear tooth root residual stress from the mold temperature, melt temperature, injection time, packing time, and packing pressure was explored was explored. The results showed that: The selected process parameters for plastic gear tooth root of the residual stress effects in varying degrees. By optimizing the injection molding process parameters, the residual stress of injection was reduced to improve the quality of injection molding gear.

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