scholarly journals Effect of Process Parameters on Short Fiber Orientation along the Melt Flow Direction in Water-Assisted Injection Molded Part

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Haiying Zhou ◽  
Hesheng Liu ◽  
Qingsong Jiang ◽  
Tangqing Kuang ◽  
Zhixin Chen ◽  
...  
Keyword(s):  
Injection Molding ◽  
Water Temperature ◽  
Process Parameters ◽  
Fiber Orientation ◽  
Flow Direction ◽  
Water Pressure ◽  
Short Fiber ◽  
Response Surfaces ◽  
Melt Flow ◽  
Melt Temperature

The short fiber orientation (SFO) distribution in the water-assisted injection molding (WAIM) is more complicated than that in traditional injection molding due to the new process parameters. In this work, an improved fiber orientation tensor method was used to simulate the SFO in WAIM. The result was compared with the scanning electron micrograph, which was consistent with the experiments. The effect of six process parameters, including filling time, melt temperature, mold temperature, delay time, water pressure, and water temperature, on the SFO along the melt flow direction were studied through orthogonal experimental design, range analysis, and variance analysis. An artificial neural network was used to establish the nonlinear agent model between the process parameters and A11 representing the fiber orientation in melt flow direction. Results show that water pressure, melt temperature, and water temperature have significant effects on SFO. The three-dimensional (3D) response surfaces and contour plots show that the values of A11 decrease with the increase in water pressure and melt temperature and increase as the water temperature rises.

scholarly journals Numerical Simulation during Short-Shot Water-Assisted Injection Molding Based on the Overflow Cavity for Short-Glass Fiber-Reinforced Polypropylene

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhong Yu ◽  
He-Sheng Liu ◽  
Tang-Qing Kuang ◽  
Xing-Yuan Huang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Injection Molding ◽  
Fiber Orientation ◽  
Delay Time ◽  
Flow Direction ◽  
Water Injection ◽  
Injection Pressure ◽  
Melt Flow ◽  
Melt Temperature ◽  
Short Shot ◽  
Main Cavity

Compared with water penetration condition of short-shot water-assisted injection molding with or without overflow cavity, it can be known from theory and common knowledge that short-shot water-assisted injection molding with overflow cavity has many advantages, such as it can save materials and energy. Then, the effects of melt short shot size, water injection delay time, melt temperature and water injection pressure on the penetration of water after penetration, and the orientation distribution of short fibers during water-assisted injection molding of the overflow cavity short-shot method were studied. It is found that the melt short shot size had the greatest influence on it, followed by water injection pressure, water injection delay time, and finally, melt temperature. With the increase of the melt short shot size, the thickness of the residual wall of the whole main cavity becomes thinner, the orientation of short fiber along the melt flow direction becomes higher, and the degree of fiber orientation changes becomes lower. In the front half of the main cavity, with the decrease of water injection pressure, the delay time of water injection, and the melt temperature, in the front part of the main cavity, the residual wall thickness becomes thinner, the fiber orientation along the melt flow direction becomes lower, and the fiber orientation changes degree becomes higher; in the latter half of the main cavity, the influence of the water penetration and the orientation distribution of short fibers along the melt flow direction are not significant.

Study on one Dimensional Numerical Simulation in Filling Stage of Water-Assisted Injection Molding

2011 ◽  
Vol 179-180 ◽  
pp. 1193-1198 ◽  
Author(s):  
Tang Qing Kuang
Keyword(s):  
Numerical Simulation ◽  
Injection Molding ◽  
Water Temperature ◽  
Water Pressure ◽  
Mold Temperature ◽  
Processing Parameters ◽  
Melt Temperature ◽  
Filling Stage ◽  
Short Shot ◽  
Temperature Water

Water assisted injection molding is a pretty novel way to fabricate hollow or more complicated parts. Its molding window and process control are more critical and difficult since additional processing parameters are involved. A simulation model for the filling stage of a pipe cavity during short-shot water assisted injection molding was proposed. The finite element/finite difference/control volume methods were adopted for the numerical simulation. A numerical study, based on the single factor method, was conducted to characterize the effect of different processing parameters on the short shot water-assisted injection-molding of thermoplastic composites, including short-shot size, melt temperature, mold temperature, water temperature and water pressure. For the factors selected in the simulations, short-shot size was found to be the principal parameters affecting the water penetration length while melt temperature, mold temperature, water temperature, water pressure were found to have little effect on the penetration of water.

scholarly journals Study of Residual Wall Thickness and Multiobjective Optimization for Process Parameters of Water-Assisted Injection Molding

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jiangen Yang ◽  
Shengrui Yu ◽  
Ming Yu
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Multiobjective Optimization ◽  
Process Parameters ◽  
Wall Thickness ◽  
Rbf Neural Network ◽  
Water Pressure ◽  
Pareto Frontier ◽  
Melt Temperature ◽  
Important Indicator

Residual wall thickness is an important indicator for water-assisted injection molding (WAIM) parts, especially the maximization of hollowed core ratio and minimization of wall thickness difference which are significant optimization objectives. Residual wall thickness was calculated by the computational fluid dynamics (CFD) method. The response surface methodology (RSM) model, radial basis function (RBF) neural network, and Kriging model were employed to map the relationship between process parameters and hollowed core ratio, and wall thickness difference. Based on the comparison assessments of the three surrogate models, multiobjective optimization of hollowed core ratio and wall thickness difference for cooling water pipe by integrating design of experiment (DOE) of optimized Latin hypercubes (Opt LHS), RBF neural network, and particle swarm optimization (PSO) algorithm was studied. The research results showed that short shot size, water pressure, and melt temperature were the most important process parameters affecting hollowed core ratio, while the effects of delay time and mold temperature were little. By the confirmation experiments for the best solution resulted from the Pareto frontier, the relative errors of hollowed core ratio and wall thickness are 2.2% and 3.0%, respectively. It demonstrated that the proposed hybrid optimization methodology could increase hollowed core ratio and decrease wall thickness difference during the WAIM process.

Warpage and Shrinkage Analysis and Optimization of Rapid Tooling Molded thin Wall Component Using Modified Particle Swarm Algorithm

2019 ◽  
Vol 18 (01) ◽  
pp. 85-102 ◽  
Author(s):  
Sagar Kumar ◽  
Amit Kumar Singh
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Simulation Analysis ◽  
Statistical Significance ◽  
Particle Swarm ◽  
Processing Parameters ◽  
Particle Swarm Algorithm ◽  
Thin Wall ◽  
Polybutylene Terephthalate ◽  
Melt Temperature

This paper presents a systematic methodology to determine optimal injection molding conditions for minimum warpage and shrinkage in a thin wall relay part using modified particle swarm optimization algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) were injected in a thin wall relay component for different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (3[Formula: see text] orthogonal array is used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage is developed in terms of the above process parameters using regression analysis. ANOVA analysis is performed to establish statistical significance within the injection molding parameters. The analytical model is further optimized using a newly developed MPSO algorithm and the process parameters values are predicted for minimizing shrinkage and warpage. The predicted values of shrinkage and warpage using MPSO algorithm are improved by approximately 30% as compared to the initial simulation values and comparable to previous literature results.

scholarly journals Optimization of Process Parameters for Vertical-Faced Polypropylene Bottle Injection Molding

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Youmin Wang ◽  
Zhichao Yan ◽  
Xuejun Shan
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Dwell Time ◽  
Optimization Problem ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Orthogonal Test ◽  
Injection Molding Process ◽  
Injection Time ◽  
Melt Temperature

In order to obtain the optimal combination of process parameters for vertical-faced polypropylene bottle injection molding, with UG, the model of the bottle was drawn, and then, one module and sixteen-cavity injection molding system was established and analyzed using Moldflow. For filling and maintaining pressure during the process of infusion bottle injection molding, the orthogonal test table L25 (56) using CAE was designed for injection molding of the bottle, with six parameters such as melt temperature, mold temperature, injection pressure, injection time, dwell pressure, and dwell time as orthogonal test factors. By finding the best combination of process parameters, the orthogonal experiment was completed, the results were analyzed by range analysis, and the order of influence of each process parameter on each direction of optimization was obtained. The prediction dates of the infusion bottle were gained under various parameters, a comprehensive quality evaluation index of the bottle was formulated, and the multiobjective optimization problem of injection molding process was transformed into a single-objective optimization problem by the integrated weighted score method. The bottle parameters were optimized by analyzing the range date of the weighted scoring method, and the best parameter combination such as melt temperature 200°C, mold temperature 80°C, injection pressure 40 MPa, injection time 2.1 S, dwell pressure 40 MPa, and dwell time 40 S was gained.

Development of the layered structure in a double-gated glass fiber-reinforced polypropylene injection molding: Experimental and simulated results

2018 ◽  
Vol 37 (14) ◽  
pp. 945-959 ◽  
Author(s):  
MC Quintana ◽  
MP Frontini
Keyword(s):  
Glass Fiber ◽  
Layered Structure ◽  
Fiber Orientation ◽  
Flow Direction ◽  
Weld Line ◽  
Orientation Distribution ◽  
Simulation Software ◽  
Distribution Data ◽  
Melt Temperature ◽  
Fiber Orientation Distribution

The present study aims to experimentally validate numerical simulation of fiber orientation distribution performed by molding simulation software Moldex3D in a double-gated injection-molded glass fiber-filled (40 wt%) polypropylene box, by making a detailed comparison of predicted and experimentally measured fiber orientation distribution data. The modeling approach evaluated in this work consists in the implementation of the Folgar–Tucker rotary diffusion model with the invariant-based optimal fitting closure approximation for the fourth-order orientation tensor. The specimen used has a weld line in the center and sharp corners. This investigation characterizes in detail the development of the through-thickness layered structure at distinctive locations of the specimen. The sensitivity of fiber orientation distribution and the layered structure to changes upon injection time and melt temperature is also evaluated. The boxes display the typical layered laminate structure, with fibers aligned in the main flow direction near the walls (shell layer) and less oriented in the middle plane (core layer). The boxes injected at the lowest melt temperature display an additional skin layer. Unfortunately, simulation fails in predicting the five layers structure developed under these latter conditions. The grade of fiber orientation is deemed to be independent of process parameters but not the layered structure.

scholarly journals Investigation on the Fiber Orientation Distributions and Their Influence on the Mechanical Property of the Co-Injection Molding Products

Polymers ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 24
Author(s):  
Chao-Tsai Huang ◽  
Xuan-Wei Chen ◽  
Wei-Wen Fu
Keyword(s):  
Injection Molding ◽  
Tensile Properties ◽  
Fiber Orientation ◽  
Flow Direction ◽  
Rapid Development ◽  
Injection System ◽  
Single Shot ◽  
Fiber Morphology ◽  
Orientation Tensor ◽  
Orientation Distributions

In recent years, due to the rapid development of industrial lightweight technology, composite materials based on fiber reinforced plastics (FRP) have been widely used in the industry. However, the environmental impact of the FRPs is higher each year. To overcome this impact, co-injection molding could be one of the good solutions. But how to make the suitable control on the skin/core ratio and how to manage the glass fiber orientation features are still significant challenges. In this study, we have applied both computer-aided engineering (CAE) simulation and experimental methods to investigate the fiber feature in a co-injection system. Specifically, the fiber orientation distributions and their influence on the tensile properties for the single-shot and co-injection molding have been discovered. Results show that based on the 60:40 of skin/core ratio and same materials, the tensile properties of the co-injection system, including tensile stress and modulus, are a little weaker than that of the single-shot system. This is due to the overall fiber orientation tensor at flow direction (A11) of the co-injection system being lower than that of the single-shot system. Moreover, to discover and verify the influence of the fiber orientation features, the fiber orientation distributions (FOD) of both the co-injection and single-shot systems have been observed using micro-computerized tomography (μ-CT) technology to scan the internal structures. The scanned images were further utilizing Avizo software to perform image analyses to rebuild the fiber structure. Specifically, the fiber orientation tensor at flow direction (A11) of the co-injection system is about 89% of that of the single-shot system in the testing conditions. This is because the co-injection part has lower tensile properties. Furthermore, the difference of the fiber orientation tensor at flow direction (A11) between the co-injection and the single-shot systems is further verified based on the fiber morphology of the μ-CT scanned image. The observed result is consistent with that of the FOD estimation using μ-CT scan plus image analysis.

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

Particle-level simulation for the prediction of short fiber orientation in injection molding

2020 ◽  
Vol 139 ◽  
pp. 106115
Author(s):  
Toshiki Sasayama ◽  
Norikazu Sato ◽  
Yoshihide Katagiri ◽  
Yuko Murayama
Keyword(s):  
Injection Molding ◽  
Fiber Orientation ◽  
Short Fiber ◽  
Particle Level

Influence of Injection Molding Parameters on Mechanical Properties of Low Density Polyethylene Filled With Multiwalled Carbon Nanotubes

2011 ◽  
Author(s):  
Catalin Fetecau ◽  
Felicia Stan ◽  
Daniel Dobrea ◽  
Dan Catalin Birsan
Keyword(s):  
Mechanical Properties ◽  
Carbon Nanotubes ◽  
Injection Molding ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Flow Direction ◽  
Mold Temperature ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Melt Temperature

In this paper, we investigated the effect of injection molding parameters such as melt temperature, mold temperature, injection speed and holding pressure on the mechanical properties of low density polyethylene reinforced with 2.5 wt% multi-walled carbon nanotubes. The Taguchi methodology with four factors and two levels was used for the design of the injection molding experiments. The mechanical properties were evaluated by tensile tests in the flow direction at room temperature (23 °C) at crosshead speeds of 1 and 5 mm/min. It was found that the mechanical properties can be modified by manipulating the injection molding parameters. The Young’s modulus of the LDPE-MWNTs composite decreased as the melt temperature increased, while mold temperature, injection molding speed and holding pressure have a moderate influence on the Young’s modulus.

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