scholarly journals Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Junhui Liu ◽  
Xindu Chen ◽  
Zeqin Lin ◽  
Shipu Diao
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Fitness Function ◽  
Injection Molding Process ◽  
Optical Performance ◽  
Surface Waviness ◽  
Molding Process ◽  
Nsga Ii ◽  
Optical Lens

Injection molding process parameters (IMPP) have a significant effect on the optical performance and surface waviness of precision plastic optical lens. This paper presents a set of procedures for the optimization of IMPP, with haze ratio (HR) reflecting the optical performance and peak-to-valley 20 (PV20) reflecting the surface waviness as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the results were analyzed by ANOVA to screen out the IMPP having a significant effect on the objectives. Then, the 34 full-factor experiment was conducted on the key IMPP, and the experimental results were used as the training and testing samples. The BPNN algorithm and the M-SVR algorithm were applied to establish the mapping relationships between the IMPP and objectives. Finally, the multiple-objective optimization was performed by applying the nondominated sorting genetic algorithm (NSGA-II), with the built M-SVR models as the fitness function of the objectives, to obtain a Pareto-optimal set, which improved the quality of plastic optical lens comprehensively. Through the experimental verification on the optimization results, the mean prediction error (MPE) of HR and PV20 is 7.16% and 9.78%, respectively, indicating that the optimization method has high accuracy.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

Multi-Layer Counter-Pressure Injection Molding for Thick-Walled Optical Lens

2021 ◽  
Vol 36 (2) ◽  
pp. 131-136
Author(s):  
Y. Liu ◽  
L. Cao ◽  
W.-K. Chi ◽  
L.-Y. Zhang ◽  
W.-M. Yang ◽  
...  
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Optical Performance ◽  
Molding Process ◽  
Optical Lens ◽  
Optical Prism ◽  
Counter Pressure ◽  
Pressure Injection ◽  
Critical Issues ◽  
Optical Applications

Abstract In precision optical applications, plastics thick-walled optical lenses are increasing. Dimensional stability and optical performance are the critical issues that should be addressed for plastic thick-walled lenses. A novel multi-layer counter-pressure injection molding process is proposed in this study. The experimental prism mold with moveable pistons was developed to investigate the effects of layering methods, counter-pressure and their combination on thick-walled optical lenses. The experimental results reveal that counter-pressure injection molding is effective in improving shrinkage, transmittance and refractive index of the thick-walled optical prism. Counter-pressure of the piston provided lower melt velocity and shorter flow path of melt to improve polymer molecules orientation, and also offered continuous holding pressure during the filling stage to eliminate defects such as shrinkage or short shots. The combination of counter-pressure and multi-layer injection molding technology further improved the dimension stability and optical performance of the thick-walled optical lens. Much thinner layers than the final wall thickness of prism ensures shrinkage reduction during the cooling stages. A thick-walled optical prism was fabricated successfully upon applying a multi-layer counter-pressure injection molding process.

Application of Optimization Algorithms in Injection Molding Process Parameters Optimization

2011 ◽  
Vol 704-705 ◽  
pp. 183-190
Author(s):  
Ze Hao Hu ◽  
Wei Wei ◽  
Juan Liu ◽  
Kun Liu
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Design Method ◽  
Orthogonal Experiment ◽  
Parameters Optimization ◽  
Injection Molding Process ◽  
Molding Process ◽  
Bp Artificial Neural Network ◽  
Agent Model ◽  
Set Up

In this paper, the numerical simulation and calculation of injection molding process are taken in the Moldflow software. The BP artificial neural network combining with the orthogonal experiment design method is used to set up the injection molding process agent model, genetic algorithms are applied to realize global optimization, finally, the optimal combination of process parameters of each quality indicators is obtained.

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.

Simulation Analysis of Residual Stress of the Plastic Gear Based on Moldflow

2012 ◽  
Vol 501 ◽  
pp. 339-343 ◽  
Author(s):  
Meng Li ◽  
Hui Min Zhang ◽  
Yong Nie
Keyword(s):  
Residual Stress ◽  
Injection Molding ◽  
Process Parameters ◽  
Simulation Analysis ◽  
Gear Tooth ◽  
Orthogonal Experiment ◽  
Injection Molding Process ◽  
Tooth Root ◽  
Molding Process ◽  
Melt Temperature

Through the orthogonal experiment with Moldflow software, numerical simulation was conducted in different injection molding process parameters. The influence on the plastic gear tooth root residual stress from the mold temperature, melt temperature, injection time, packing time, and packing pressure was explored was explored. The results showed that: The selected process parameters for plastic gear tooth root of the residual stress effects in varying degrees. By optimizing the injection molding process parameters, the residual stress of injection was reduced to improve the quality of injection molding gear.

scholarly journals Constant Temperature Approach for the Assessment of Injection Molding Parameter Influence on the Fatigue Behavior of Short Glass Fiber Reinforced Polyamide 6

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1569
Author(s):  
Selim Mrzljak ◽  
Alexander Delp ◽  
André Schlink ◽  
Jan-Christoph Zarges ◽  
Daniel Hülsbusch ◽  
...  
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Fatigue Properties ◽  
Injection Molding Process ◽  
Molding Process ◽  
Short Glass Fiber ◽  
Glass Fiber Reinforced ◽  
Molding Parameter ◽  
Short Glass

Short glass fiber reinforced plastics (SGFRP) offer superior mechanical properties compared to polymers, while still also enabling almost unlimited geometric variations of components at large-scale production. PA6-GF30 represents one of the most used SGFRP for series components, but the impact of injection molding process parameters on the fatigue properties is still insufficiently investigated. In this study, various injection molding parameter configurations were investigated on PA6-GF30. To take the significant frequency dependency into account, tension–tension fatigue tests were performed using multiple amplitude tests, considering surface temperature-adjusted frequency to limit self-heating. The frequency adjustment leads to shorter testing durations as well as up to 20% higher lifetime under fatigue loading. A higher melt temperature and volume flow rate during injection molding lead to an increase of 16% regarding fatigue life. In situ Xray microtomography analysis revealed that this result was attributed to a stronger fiber alignment with larger fiber lengths in the flow direction. Using digital volume correlation, differences of up to 100% in local strain values at the same stress level for different injection molding process parameters were identified. The results prove that the injection molding parameters have a high influence on the fatigue properties and thus offer a large optimization potential, e.g., with regard to the component design.

Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

2013 ◽  
Vol 345 ◽  
pp. 586-590 ◽  
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Axial Deformation ◽  
Molding Process ◽  
Melt Temperature ◽  
Bp Network ◽  
Injection Parameters ◽  
Moldflow Simulation

To obtain optimal injection process parameters, GA was used to optimize BP network structure based on Moldflow simulation results. The BP network was set up which considering the relationship between volume shrinkage of plastic parts and injection parameters, such as mold temperature, melt temperature, holding pressure and holding time etc. And the optimal process parameters are obtained, which is agreed with actual results. Using BP network to predict injection parameters impact on parts quality can effectively reduce the difficulty and workload of other modeling methods. This method can be extended to other quality prediction in the process of plastic parts.Keyword: Genetic algorithm (GA);Neural network algorithm (BP);Injection molding process optimization;The axial deformation

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