Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method

2007 ◽  
Vol 183 (2-3) ◽  
pp. 412-418 ◽  
Author(s):  
Changyu Shen ◽  
Lixia Wang ◽  
Qian Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Injection Molding ◽  
Process Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Genetic Algorithm Method ◽  
Artificial Neural

scholarly journals Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4158
Author(s):  
Mehdi Moayyedian ◽  
Ali Dinc ◽  
Ali Mamedov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Holding Time ◽  
Injection Molding Process ◽  
Molding Process ◽  
Filling Time ◽  
Thin Walled

Plastics are commonly used engineering materials, and the injection-molding process is well known as an efficient and economic manufacturing technique for producing plastic parts with various shapes and complex geometries. However, there are certain manufacturing defects related to the injection-molding process, such as short shot, shrinkage, and warpage. This research aims to find optimum process parameters for high-quality end products with minimum defect possibility. The Artificial Neural Network and Taguchi Techniques are used to find a set of optimal process parameters. The Analytic Hierarchy Process is used to calculate the weight of each defect in the proposed thin-walled part. The Finite Element Analysis (FEA) using SolidWorks plastics is used to simulate the injection-molding process for polypropylene parts and validate the proposed optimal set of process parameters. Results showed the best end-product quality was achieved at a filling time of 1 s, cooling time of 3 s, pressure-holding time of 3 s, and melt temperature of 230 °C. The end-product quality was mostly influenced by filling time, followed by the pressure-holding time. It was found that the margin of error for the proposed optimization methods was 1.5%, resulting from any uncontrollable parameters affecting the injection-molding process.

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.

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