An Intelligent Optimization System for PIM Process

2019 ◽  
Vol 814 ◽  
pp. 203-210
Author(s):  
Wen Chin Chen ◽  
Tai Hao Chen ◽  
Ding Tsair Chang ◽  
Manh Hung Nguyen
Keyword(s):  
Experimental Data ◽  
Injection Molding ◽  
Taguchi Method ◽  
Injection Molding Process ◽  
Optimal Process ◽  
Molding Process ◽  
Training Time ◽  
Intelligent Optimization ◽  
Optimization System ◽  
Modified Pso

This study proposes an intelligent optimization system based on the Taguchi method, back-propagation neural network (BPNN), multilayer perceptron (MLP) and modified PSO-GA to find optimal process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is used to determine the initial combination of parameter settings by calculating the signal-to-noise (S/N) ratios from the experimental data. Significant factors are determined using analysis of variance (ANOVA). The S/N ratio predictors (BPNNS/N) and quality predictors (BPNNQ) are constructed using BPNN with the experimental data. In addition, a modified PSO-GA algorithm in conjunction with MLP is used to find initial weights of BPNN and to reduce the training time of BPNN. In the first stage optimization, the S/N ratio predictors are coupled with GA to reduce the variations of the manufacturing process. In the second stage optimization, The combination of S/N ratio predictors and quality predictors with modified PSO-GA is empoyed to search for the optimal parameters. Finally, three confirmation experiments are performed to assess the effectiveness of these approaches. The experimental results show that the proposed system can create the best performance, and optimal process parameter settings which not only enhance the stability in the whole injection molding process but also effectively improve the PIM product quality. Furthermore, experiences of the novel hybrid optimization system can be transferred into the intelligent PIM machines for the coming up internet of things (IoT) and big data environment.

Optimizing Injection Parameters of Kenaf Filler Polypropylene Composite by Taguchi Method

2017 ◽  
Vol 894 ◽  
pp. 81-84 ◽  
Author(s):  
Mohd Khairul Fadzly Md Radzi ◽  
Norhamidi Muhamad ◽  
Abu Bakar Sulong ◽  
Zakaria Razak
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Process Condition ◽  
Injection Pressure ◽  
Poor Quality ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Parameters ◽  
Pp Composites ◽  
Injection Temperature

Optimization of injection molding parameters provided a solution to achieve strength improvement of kenaf filler polypropylene composites. Since, molded polymers composites possibility being effected by machine parameters and other process condition that may cause poor quality of composites product. Thus in this study, composite of kenal filler reinforced with thermoplastic polypropylene (PP) were prepared using a sigma blade mixer, followed by an injection molding process. To determine the optimal processing of injection parameters, Taguchi method with L27 orthogonal array was used on statistical analysis of tensile properties of kenaf/PP composites. The results obtained the optimum parameters which were injection temperature 190°C, injection pressure 1300 bar, holding pressure 1900 bar and injection rate 20cm3/s. From the analysis of variance (ANOVA), both flow rate and injection temperature give highest contribution factor to the mechanical properties of the kenaf/PP composites.

An Intelligent Optimization Controller for a Non-Linear System: Application to an Injection Molding Machine

2007 ◽  
Vol 10-12 ◽  
pp. 884-888 ◽  
Author(s):  
J.M. Liang
Keyword(s):  
Linear System ◽  
Injection Molding ◽  
Complex Dynamics ◽  
Parameter Tuning ◽  
Injection Molding Process ◽  
Molding Process ◽  
Intelligent Optimization ◽  
Non Linear ◽  
Line Process ◽  
Non Linear System

The injection molding process has been well-known non-linear complex dynamics and the approach extensively applied manual control and rely on experienced engineers. An intelligent optimization controller has been designed with two series neural networks and the multi-losses function has been proven can automatically adjust the machine setting overcome the complex dynamics to upgrade part’s quality and reduce experienced engineers. The proposed method has shown promising future for expediting the on-line process parameter tuning work to other complicate non-linear system in the future.

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.

Injection Molding of Rubber Compounds: Experimentation and Simulation

1991 ◽  
Vol 64 (2) ◽  
pp. 296-324 ◽  
Author(s):  
J. S. Deng ◽  
A. I. Isayev
Keyword(s):  
Experimental Data ◽  
Injection Molding ◽  
Induction Time ◽  
Flow Simulation ◽  
Injection Molding Process ◽  
Model Parameters ◽  
Large Sample Size ◽  
Molding Process ◽  
Rubber Compounds ◽  
Pressure Development

Abstract Results of experimental and theoretical studies of injection molding of rubber compounds have been reported. Characterizations on the rheological properties and the vulcanization kinetics of rubber compounds were carried out by means of MPT and DSC, respectively. The models were employed to fit these experimental data. An attempt has been made in extending the proposed empirical kinetic model based on DSC data to similar curing data obtained by means of the MDR technique. The heat-transfer effect due to the large sample size used in MDR measurements has been found to have a small effect on the kinetic data. Due to the different principle of state-of-cure measurements in MDR and DSC, the model parameters of curing kinetics have been found to be different in these measurements. A two-dimensional flow simulation of generalized Newtonian fluids based on both finite-difference and finite-element methods has been performed. The pressure development at various positions along the flow path during the filling stage of the molds was obtained experimentally for various injection speeds. The predicted results on pressure development during cavity filling showed qualitative agreement with the experimental data. Possible reasons for the observed discrepancy in pressure traces are: neglect of local extra pressure losses (in the juncture sections), compressibility of rubber compounds, leakage (back-flow) of material during injection, and voids formation in the injection chamber. The study on the vulcanization behavior of rubber compounds during injection molding process has also been done. The proposed empirical kinetic and induction time models were able to satisfactorily predict the cure levels of molded rubber products. At the same time, the results predicted by employing nth order kinetics were found to be unsatisfactory. The contribution of nonisothermal induction time in calculating cure levels of the molded rubber products was found to be significant. The mechanical properties and anisotropy have been investigated for two rubber compounds. It is suggested that there exists a mold temperature at which the properties and cycle times are optimal, and the filler type shows a significant effect on the tensile modulus. The rubber moldings were found to be highly anisotropic in mechanical behaviors. The anisotropy could be reduced significantly at high injection speed due to the faster stress-relaxation process.

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