Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

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.

Download Full-text

Notice of Retraction: Optimization of injection molding process parameters based on Response Surface Methodology and genetic algorithm

2010 2nd International Conference on Computer Engineering and Technology ◽

10.1109/iccet.2010.5486093 ◽

2010 ◽

Author(s):

Baoshou Sun ◽

Zhenfan Wu ◽

Boqin Gu ◽

Xiaodiao Huang

Keyword(s):

Genetic Algorithm ◽

Response Surface Methodology ◽

Injection Molding ◽

Response Surface ◽

Process Parameters ◽

Injection Molding Process ◽

Molding Process

Download Full-text

Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

Polymers ◽

10.3390/polym13223874 ◽

2021 ◽

Vol 13 (22) ◽

pp. 3874

Author(s):

Yan-Mao Huang ◽

Wen-Ren Jong ◽

Shia-Chung Chen

Keyword(s):

Neural Network ◽

Injection Molding ◽

Transfer Learning ◽

Analysis Data ◽

Mold Temperature ◽

Training Data ◽

Injection Molding Process ◽

Backpropagation Neural Network ◽

Molding Process ◽

Injection Speed

This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the Z-axis, shrinkage along the X-axis, and shrinkage along the Y-axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.

Download Full-text

ANALYSIS OF INJECTION MOLDING PROCESS TO REDUCED DEFECTS (SHORT-SHOT)

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v5.i6.2018.251 ◽

2020 ◽

Vol 5 (6) ◽

pp. 113-119

Author(s):

L.D.Mahajan ◽

P.N.Ulhe

Keyword(s):

Injection Molding ◽

Process Parameters ◽

Injection Pressure ◽

Quality Characteristic ◽

Injection Molding Process ◽

Molding Process ◽

Plastic Specimen ◽

Optimal Injection ◽

Short Shot

This paper deals with optimal injection molding process parameters for minimum short shot. In this study, analyses of injection molding process parameters were carried out to reduced defects and minimize short shots. Optimal injection molding conditions for minimum short shot were determined by the DOE technique of Taguchi and the analysis of variance (ANOVA) methods. For this study CPVC plastic specimen was tested. Determination of the optimal Injection molding process parameters were based on S/N ratios. According to results mold closing speed had significant effect on quality characteristic. Mold pressure and Injection pressure had no significant effect

Download Full-text

A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-012-3933-6 ◽

2012 ◽

Vol 63 (5-8) ◽

pp. 691-704 ◽

Cited By ~ 31

Author(s):

Chorng-Jyh Tzeng ◽

Yung-Kuang Yang ◽

Yu-Hsin Lin ◽

Chih-Hung Tsai

Keyword(s):

Neural Network ◽

Response Surface Methodology ◽

Injection Molding ◽

Response Surface ◽

Process Parameters ◽

Injection Molding Process ◽

Molding Process

Download Full-text

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

Polymers ◽

10.3390/polym13234158 ◽

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.

Download Full-text

Multi-Objective Optimizations for Microinjection Molding Process Parameters of Biodegradable Polymer Stent

Materials ◽

10.3390/ma11112322 ◽

2018 ◽

Vol 11 (11) ◽

pp. 2322 ◽

Cited By ~ 2

Author(s):

Hongxia Li ◽

Kui Liu ◽

Danyang Zhao ◽

Minjie Wang ◽

Qian Li ◽

...

Keyword(s):

Residual Stress ◽

Injection Molding ◽

Process Parameters ◽

Optimization Method ◽

Injection Molding Process ◽

Kriging Surrogate Model ◽

Adaptive Optimization ◽

Molding Process ◽

Microinjection Molding ◽

Design Variables

Microinjection molding technology for degradable polymer stents has good development potential. However, there is a very complicated relationship between molding quality and process parameters of microinjection, and it is hard to determine the best combination of process parameters to optimize the molding quality of polymer stent. In this study, an adaptive optimization method based on the kriging surrogate model is proposed to reduce the residual stress and warpage of stent during its injection molding. Integrating design of experiment (DOE) methods with the kriging surrogate model can approximate the functional relationship between design goals and design variables, replacing the expensive reanalysis of the stent residual stress and warpage during the optimization process. In this proposed optimization algorithm, expected improvement (EI) is used to balance local and global search. The finite element method (FEM) is used to simulate the micro-injection molding process of polymer stent. As an example, a typical polymer vascular stent ART18Z was studied, where four key process parameters are selected to be the design variables. Numerical results demonstrate that the proposed adaptive optimization method can effectively decrease the residual stress and warpage during the stent injection molding process.

Download Full-text