Optimization of Processing Parameters in Injection Molding Based on Adaptive Ant Colony Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 304-310 ◽  
Author(s):  
Feng Li Huang ◽  
Shui Sheng Chen ◽  
Jin Mei Gu
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Kriging Model ◽  
Ant Colony ◽  
Injection Molding Process ◽  
Adaptive Genetic Algorithm ◽  
Molding Process ◽  
Correlation Degree

An optimization method, integrating correlation degree, response surface method and ant colony algorithm, is proposed for exploring optimal parameters with the molding quality evaluation of warpage amount in injection molding. Initially, a novel formula calculating correlation degree is brought forth on the basis of the definition of distance and place value, and the parameters are chosen using the correlation degree method. Then the approximate model of the injection molding process is constructed by Kriging model with the determined parameters. Finally, the adaptive genetic algorithm and the ant colony algorithm are adopted to solve the optimization problem respectively, and injection molding tests are experimentally performed to validate the optimization results of parameters in injection molding. The experimental results demonstrate that the ant colony algorithm is superior to the genetic algorithm in solving the optimization problem for the low-dimensional design variables vector and the short coding length.

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1761-1764
Author(s):  
Feng Kai Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Route Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Logistics Distribution ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

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 Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Peng Zhao ◽  
Zhengyang Dong ◽  
Jianfeng Zhang ◽  
Yi Zhang ◽  
Mingyi Cao ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Optimization Problem ◽  
Classification Problem ◽  
Support Vector ◽  
Injection Molding Process ◽  
Average Weight ◽  
Optimization Strategy ◽  
Molding Process ◽  
Injection Molded

Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.

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