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

Materials ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2322 ◽  
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.

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.

Parameters Optimization for Injection Molding Based on Digital Signal Processing

2013 ◽  
Vol 433-435 ◽  
pp. 1890-1893 ◽  
Author(s):  
Wen Ling Xie ◽  
Shun Yong Zhou ◽  
Yong Hu
Keyword(s):  
Signal Processing ◽  
Injection Molding ◽  
Digital Signal Processing ◽  
Process Parameters ◽  
Digital Signal ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Practical Significance ◽  
Injection Molding Process ◽  
Molding Process

The parameters optimization for injection molding based on digital signal processing is gave in this paper. First, design the optimization program of injection molding parameters with orthogonal test; Second, gating system, cooling systems and related injection molding process parameters are chosen as the experimental analysis factors to optimize injection molding process, and analyze the range and variance of test data by MATLAB, to determine the factors that affect the forming quality of the process significantly; Third, the model of process optimization is established by regression analysis; Finally, the optimal process parameters is found by genetic algorithm. The experimental results shown this optimization method can achieved the best parameters for practical produce. So, the method has important practical significance.

Optimization of Plastic Injection Molding Process Parameters for Thin-Wall Plastics Injection Molding

2009 ◽  
Vol 69-70 ◽  
pp. 525-529 ◽  
Author(s):  
Jie Jin ◽  
H.Y. Yu ◽  
S. Lv
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Optimization Method ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Molding Process ◽  
Taguchi Optimization ◽  
Taguchi Optimization Method ◽  
Plastics Injection Molding ◽  
Injection Molded

The effects of the process parameters on the warpge and shrinkage of parts in different thickness are analyzed by Taguchi optimization method. Taguchi optimization method was used for exploiting mold analysis based on three level factorial designs. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are utilized to find the optimal levels and the effect of process parameters on warpage. It can be concluded that Taguchi method is suitable to solve the quality problem of the injection-molded thermoplastic parts.

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.

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.

Effect of gas counter pressure on shrinkage and residual stress for injection molding process

2017 ◽  
Vol 37 (5) ◽  
pp. 505-520 ◽  
Author(s):  
Wen-Ren Jong ◽  
Shyh-Shin Hwang ◽  
Ming-Chieh Tsai ◽  
Chien-Chou Wu ◽  
Chi-Hung Kao ◽  
...  
Keyword(s):  
Residual Stress ◽  
Injection Molding ◽  
Flow Direction ◽  
Injection Molding Process ◽  
Molding Process ◽  
Daily Lives ◽  
Counter Pressure ◽  
Packing Pressure ◽  
Sink Marks ◽  
Feedback Data

Abstract Plastic products are common in contemporary daily lives. In the plastics industry, the injection molding process is advantageous for features such as mass production and stable quality. The problem, however, is that the melt will be affected by the residual stress and shrinkage generated in the process of filling and cooling; hence, defects such as warping, deformation, and sink marks will occur. In order to reduce product deformation and shrinkage during the process of molding, the screw of the injection molding machine will start the packing stage when filling is completed, which continuously pushes the melt into the cavity, thus making up for product shrinkage and improving their appearance, quality, and strength. If the packing pressure is too high, however, the internal residual stress will increase accordingly. This study set out to apply gas counter pressure (GCP) in the injection molding process. By importing gas through the ends of the cavity, the melt was exposed to a melt front pressure, which, together with the packing pressure from the screw, is supposed to reduce product shrinkage. The aim was to investigate the impacts of GCP on the process parameters via the changes in machine feedback data, such as pressure and the remaining injection resin. This study also used a relatively thin plate-shaped product and measurements, such as the photoelastic effect and luminance meter, to probe into the impacts of GCP on product residual stress, while a relatively thick paper-clip-shaped product was used to see the impacts of GCP on shrinkage in thick parts. According to the experimental results, the addition of GCP resulted in increased filling volume, improvement of product weight and stability, and effective reduction of section shrinkage, which was most obvious at the point closest to the gas entrance. The shrinkage of the sections parallel and vertical to the flow direction was proved to be reduced by 32% and 16%, respectively. Moreover, observations made via the polarizing stress viewer and luminance meter showed that the internal residual stress of a product could be effectively reduced by a proper amount of GCP.

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