A hybrid back-propagation neural network and intelligent algorithm combined algorithm for optimizing microcellular foaming injection molding process parameters

2020 ◽  
Vol 50 ◽  
pp. 528-538
Author(s):  
Wei Guo ◽  
Feng Deng ◽  
Zhenghua Meng ◽  
Lin Hua ◽  
Huajie Mao ◽  
...  
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Process Parameters ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Injection Molding Process ◽  
Intelligent Algorithm ◽  
Molding Process ◽  
Microcellular Foaming ◽  
Combined Algorithm

A Multi-Objective Quality Optimization System for Injection Molding Process Parameters

2014 ◽  
Vol 945-949 ◽  
pp. 478-483
Author(s):  
Wen Chin Chen ◽  
Yen Fu Lin ◽  
Pen Hsi Liou
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Optimal Parameter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Injection Molding Process ◽  
Second Phase ◽  
Molding Process ◽  
Optimization System ◽  
Two Phases

This study proposes an optimization system to find out the optimal process parameters of plastic injection molding (PIM). The system is divided into two phases. In the first phase, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. In the second phase, the back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the optimal parameter combination. The purpose of this stage is to reduce the process variance and promote product quality. Experimental results show that the proposed optimization system can not only satisfy the quality specification, but also improve stability of the PIM process.

Optimization of Cable Ties Injection Molding Process Using Back Propagation Neural Network and Genetic Algorithm (BPNN-GA)

2016 ◽  
Vol 836 ◽  
pp. 159-164 ◽  
Author(s):  
Arif Wahjudi ◽  
Bobby Oedy Pramoedyo Soepangkat ◽  
Yang Fitri Arriyani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Injection Molding ◽  
Process Parameters ◽  
Back Propagation ◽  
Injection Pressure ◽  
Back Propagation Neural Network ◽  
Tensile Yield Strength ◽  
Injection Flow ◽  
Nozzle Temperature

Setting parameters in the injection molding machine play an important role to the quality of cable ties product. They affect not only to the number of the rejection products but also to their tensile yield strength. The goal of this study is to obtain a combination of process parameters such as nozzle temperature, injection pressure, injection flow, and switch-over to holding pressure, which results the optimal tensile yield as the observed response using Back Propagation Neural Network-Genetic Algorithm (BPNN-GA). In this study, a 4-8-8-1 BPNN model was applied to predict the tensile yield based on a random combination of process parameters. The tensile yield then was optimized by genetic algorithm through several iterations. The optimal tensile yield of 28.44 MPa has been obtained using the following combination i.e. nozzle temperature of 250 oC, injection pressure of 1400 bar, injection flow of 40 cm3/s, and switch-over to holding pressure of 13,2 cm3.

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.

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