scholarly journals Multi-quality Index Optimization of Injectionof injection Compression Process Parameters Based on Grey Robustness

2021 ◽  
Vol 2125 (1) ◽  
pp. 012030
Author(s):  
Junjie Zhu ◽  
Wenhan Huang ◽  
Zhiwen Qiu
Keyword(s):  
Process Parameters ◽  
Quality Index ◽  
Orthogonal Experiment ◽  
Relational Theory ◽  
Molding Process ◽  
Compression Process ◽  
Sink Marks ◽  
Four Levels ◽  
Injection Compression Molding ◽  
Grey Relational

Abstract Aiming at the balance optimization of multiple mass indexes, such as sink marks, warpage and residual stress in the injection compression molding process of optical products, from the perspective of robust design, the experiment of injection compression processing of wedge-shaped light guide plate with five factors and four levels was designed by orthogonal experiment method. The optimal combination of process parameters, the order of influence and the trend of change were obtained by S/N analysis. Finally, the grey relational theory was used to transform the multi-quality index optimization problem into the single objective optimization of grey relational degree, and the optimal process parameter combination considering multiple quality indexes were obtained. Compared with before optimization, each quality index decreased by 23.89%, 8.87% and 24.68% respectively. The research shows that the optimization technology based on grey robustness can effectively solve the problem of balance optimization among multiple mass indexes, and can realize the overall optimization of injection compression process parameters.

Application of Orthogonal Test in Numerical Simulation of Glass Lens Molding

2012 ◽  
Vol 497 ◽  
pp. 245-249
Author(s):  
Dao Cheng Zhang ◽  
Ke Jun Zhu ◽  
Yong Jian Zhu ◽  
Shao Hui Yin ◽  
Jian Wu Yu
Keyword(s):  
Process Parameters ◽  
Orthogonal Experiment ◽  
Fem Simulation ◽  
Orthogonal Test ◽  
Test Method ◽  
Testing Time ◽  
Molding Process ◽  
Glass Lens ◽  
Lens Molding ◽  
Process Factors

Glass lens molding is a high-volume fabrication method for producing optical components. In this paper, combined with the orthogonal test method and finite element method (FEM) simulation, the coupled thermo-mechanical analysis was carried out to analyze the key process factors. In order to reduce the testing time, an orthogonal test with three sets of level factors and three parameters is conducted to obtain the optimal molding process parameters. The result shows that the most significant parameter is molding velocity, the other effect parameters are molding temperature and friction coefficient. According to the previous analysis of orthogonal experiment, it is shown that the best optimal finishing process parameters were A2B1C1.

scholarly journals Optimization of Process Parameters of Wire Cut Electrical Discharge Machining on Titanium Grade-5 Material using Design of Experiments

2019 ◽  
Vol 8 (11) ◽  
pp. 3937-3946 ◽  
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Orthogonal Array ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Relational Theory ◽  
Relational Analysis ◽  
Grade 5 ◽  
Titanium Grade ◽  
Grey Relational

In this paper, a compelling methodology, Taguchi grey relational analysis, was employed to the test results of wire-cut electrical discharge machining on Titanium Grade - 5 material with the consideration of multiple performance characteristics of the output response variables. The methodology merges the orthogonal array design of experiment with grey relational analysis. The primary target of this examination is to accomplish the maximization of material removal rate, minimization of both Surface roughness and kerf width. Grey relational theory is implemented to assess the optimal process parameters that improve the response measures. The test was finished by utilizing Taguchi's orthogonal array L18. Each test has been performed under various states of input parameters. The response table and grey relational grade for each level of the machining parameters have been established. From 18 tests, the best mix of parameters was identified. The results of test verify that the suggested technique in this investigation adequately develops the machining performance of Wire cut EDM process.

Influence of Injection Molding Process Parameters on Sink Marks of Injection Parts Based on Moldflow

2013 ◽  
Vol 347-350 ◽  
pp. 1163-1167
Author(s):  
Ling Bai ◽  
Hai Ying Zhang ◽  
Wen Liu
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Processing Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Packing Pressure ◽  
Sink Marks ◽  
Research Show

Moldflow software was used to obtain the best gate location and count. Influence of injection molding processing parameters on sink marks of injection-piece was studied based on orthogonal test. The effects of different process parameters were analyzed and better process parameters were obtained. Results of research show that decreasing melt temperature, mold temperature, the increasing injection time and packing pressure can effectively reduce the sink marks index.

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.

A Study on Computer Simulation of Plastic Lens Molding Process

2004 ◽  
Vol 471-472 ◽  
pp. 490-493 ◽  
Author(s):  
W. Li ◽  
Yang Fu Jin ◽  
Xun Lv ◽  
C.H. Kua
Keyword(s):  
Process Parameters ◽  
Critical Parameters ◽  
Molding Process ◽  
Packing Pressure ◽  
Experimental Works ◽  
Injection Compression Molding ◽  
Experimental Trials ◽  
Cae Simulation ◽  
Packing Time ◽  
Perfect Plastic

In this paper, some molding process parameters such as injection time, packing time, packing pressure and process temperature etc. were optimized by the Computer Aided Engineering (CAE) simulation (Moldex 3D) for injection molding of a plastic lens. Some experimental trials were carried out for verifying of the CAE simulation results with checking of the lens shrinkage and birefringence etc. as well. The results showed that, the recommended molding process parameters from CAE simulation and the actual experiments were almost the same, hence the CAE is a established tool based on the scientific approach to reduce experimental works, to identify critical parameters and to save substantial costs. Lately, a perfect plastic lens was gained by the Injection–Compression Molding process with the optimized process parameters by a 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.

Application of Optimization Algorithms in Injection Molding Process Parameters

2011 ◽  
Vol 308-310 ◽  
pp. 3-8 ◽  
Author(s):  
Ze Hao Hu ◽  
Wei Wei ◽  
Juan Liu ◽  
Kun Liu
Keyword(s):  
Process Parameters ◽  
Quality Index ◽  
Processing Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Agent Model ◽  
Training Samples ◽  
The Neural Networks ◽  
Neural Networks Ensemble ◽  
Cae Simulation

The data of process parameters and quality index from CAE simulation orthogonal test is used as training samples, the BP neural network is trained and the neural networks ensemble approximate calculation agent model of the relations between processing parameters and the quality index of product are obtained. The agent model with a clear mathematical formula calculates quickly and accurately, so it can optimize globally by the genetic algorithm. The best group of process parameters is obtained and a multi-objective optimization of the quality index of products is realized.

Optimization of Multiple Responses in Dry Turning Process Using Taguchi-Grey Relational Analysis

2014 ◽  
Vol 592-594 ◽  
pp. 620-624
Author(s):  
Sumit Verma ◽  
Hari Singh
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Tangential Force ◽  
Cutting Speed ◽  
Relational Theory ◽  
Depth Of Cut ◽  
Multiple Responses ◽  
Relational Analysis ◽  
Grey Relational ◽  
Optimal Cutting Parameters

The present study investigates the optimization of multiple responses in turning of EN-8 steel by the Taguchi and grey relational analysis. The performance characteristics considered are tangential force, feed force and radial force. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of tangential, feed, radial forces and optimal cutting parameters. An orthogonal array L18 is used for the experimental design. The setting of process parameters— nose radius, 0.8mm; cutting speed, 60.65 m/min; feed rate, 0.04 mm/rev; and depth of cut, 0.60 mm— has highest grey relational grade and therefore produces best turning performance in terms of cutting forces.

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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