The Injection Molding Simulation of Lever Cam Ass’y Using High-Functional Polymer

2007 ◽  
Vol 26-28 ◽  
pp. 1007-1010
Author(s):  
Chang Su Hahn ◽  
Sang Ho Noh ◽  
Dong Ok Kim ◽  
Yong Mun Ryu ◽  
Beom Suck Han
Keyword(s):  
Injection Molding ◽  
Optimal Shape ◽  
Functional Polymer ◽  
Selection Technique ◽  
Injection Parameter ◽  
Packing Pressure ◽  
Injection Molding Simulation ◽  
Injection Temperature ◽  
Proper Material ◽  
Time Injection

Recently the application of lever cam ass’y made of the high-functional polymer is increasing internationally to improve the productivity and weight reduction. But the application of injection molding of lever cam ass’y in our country is not reported yet because of the lack of the optimal selection technique of proper material and the optimal design of injection parameter. In order to solve the problem, the injection molding simulation is used. The modification of gate position and rib thickness was done and the parameters like injection time, injection temperature, packing pressure and cooling time were changed. As a result, the comparison between models was done and the optimal shape of lever cam ass’y was developed.

A novel method for predicting degrees of crystallinity in injection molding during packing stage

2017 ◽  
Vol 233 (1) ◽  
pp. 204-214 ◽  
Author(s):  
Peng Zhao ◽  
Weimin Yang ◽  
Xiaoman Wang ◽  
Jiangang Li ◽  
Bo Yan ◽  
...  
Keyword(s):  
Injection Molding ◽  
Prediction Method ◽  
Mold Temperature ◽  
Simulation Software ◽  
Complex Flow ◽  
Packing Pressure ◽  
Injection Molding Simulation ◽  
Novel Method ◽  
High Cooling Rates ◽  
Density Results

Being able to predict products’ degrees of crystallinity and thereby optimize their crystallization processes is of great significance for producing high-quality polymeric products in injection molding. However, it is rather difficult to theoretically establish the relationship between the crystallization results and processing conditions (high cooling rates and pressures, strong and complex flow fields). Injection molding simulation software can simulate polymers’ density results during packing stage, and these predicted density results can be used to calculate polymers’ crystallinity results. Based on this idea, a novel method was proposed to predict the degrees of crystallinity for polymers during packing stage. In this method, pressure and temperature results are first simulated by an injection molding simulation software, and then the density results are calculated based on a pressure–volume–temperature model. Next, the crystallinity results are solved according to the densities of the fully crystalline part and the purely amorphous part. Finally, two case studies are conducted to verify the proposed crystallinity prediction method. Moreover, the effects of packing parameters (mold temperature, packing pressure, and packing time) on polymers’ crystallization behaviors are investigated. The experimental results show that the proposed method is correct and effective.

Effect of the packing stage on fiber orientation for injection molding simulation of fiber-reinforced composites

2017 ◽  
Vol 31 (9) ◽  
pp. 1204-1218 ◽  
Author(s):  
Huan-Chang Tseng ◽  
Rong-Yeu Chang ◽  
Chia-Hsiang Hsu
Keyword(s):  
Injection Molding ◽  
Fiber Orientation ◽  
Mathematic Model ◽  
Fiber Reinforced Composites ◽  
Experimental Investigations ◽  
Reinforced Composites ◽  
Fiber Reinforced ◽  
Filling Stage ◽  
Packing Pressure ◽  
Injection Molding Simulation

During the packing or post-filling stage, a significant flow of polymer inside the cavity may result due to the compressibility of the polymer melt under the higher packing pressure of the injection molding process. In the meantime, the effect of the packing stage on the shell–core structure of the fiber orientation for the fiber-reinforced composites has always been a concern. Even though certain commercial packages have undergone unified simulations of the filling and packing stages, fiber orientation has usually been determined at the end of the filling stage. A recently proposed mathematic model, Improved Anisotropic Rotary Diffusion and Retarding Principal Rate, having incorporated the state-of-the-art technology of 3D injection molding simulation, has demonstrated its ability to provide reliable predictions of fiber orientation. The present numerical results concentrate on comparing and analyzing the difference in fiber orientation between the filling and packing stages, while the important effects of packing time and packing pressure are further revealed. A qualitative comparison of core thickness widths in related experimental investigations is discussed herein.

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.

Key Technologies Research on Color Contour Map of 3D Injection Molding Simulation Results

2012 ◽  
Vol 217-219 ◽  
pp. 1998-2001
Author(s):  
Tie Geng ◽  
Qing Hai Ren ◽  
Wei Qing Tu ◽  
Dan Dan Liu
Keyword(s):  
Injection Molding ◽  
Color Image ◽  
Three Dimensional ◽  
Physical Field ◽  
Contour Map ◽  
Image Rendering ◽  
Color Range ◽  
Key Technologies ◽  
Injection Molding Simulation ◽  
Simulation Results

According to the color contour map of the 3D injection molding simulation results, the commonly used color contour map drawing algorithm was researched, and a three-dimensional color image rendering algorithm which based on the "physical field values and color range mapping" was given too. And the key technologies of the algorithm which was used to draw 3D color contour map were introduced in detail. In the end, an example was given.

Study on Optical Properties of Microstructure of Lightguiding Plate for Micro Injection Molding and Micro Injection-Compression Molding

2006 ◽  
Vol 505-507 ◽  
pp. 229-234 ◽  
Author(s):  
Yung Kang Shen ◽  
H.J. Chang ◽  
C.T. Lin
Keyword(s):  
Optical Properties ◽  
Injection Molding ◽  
Digital Camera ◽  
Mold Temperature ◽  
Compression Molding ◽  
Micro Injection Molding ◽  
Melt Temperature ◽  
Packing Pressure ◽  
Injection Compression Molding ◽  
Better Than

The purpose of this paper presents the optical properties of microstructure of lightguiding plate for micro injection molding (MIM) and micro injection-compression molding (MICM). The lightguiding plate is applied on LCD of two inch of digital camera. Its radius of microstructure is from 100μm to 300μm by linearity expansion. The material of lightguiding plate uses the PMMA plastic. This paper uses the luminance distribution to make a comparison between MIM and MICM for the optical properties of lightguiding plate. The important parameters of process for optical properties are the mold temperature, melt temperature and packing pressure in micro injection molding. The important parameters of process for optical properties are the compression distance, mold temperature and compression speed in micro injection-compression molding. The process of micro injection-compression molding is better than micro injection molding for optical properties.

The Use of Advanced Aluminum Alloys for Enhanced Productivity in Plastic Injection Molding

2000 ◽  
Author(s):  
Jim Nerone ◽  
Karthik Ramani
Keyword(s):  
Aluminum Alloys ◽  
Injection Molding ◽  
Wall Thickness ◽  
Simulation Software ◽  
Cooling Time ◽  
Coolant Temperature ◽  
Steel Mold ◽  
Cooling Channels ◽  
Injection Molding Simulation ◽  
Increased Strength

Abstract New aluminum alloys, QC-7® and QE-7®, have thermal conductivities four times greater than traditional tool steels, and have significantly increased strength and hardness compared to traditional aluminum materials. Molds were constructed of P-20 tool steel and QE-7® aluminum and were used to provide experimental data regarding thermal mold characteristic and confirm injection molding simulation predictions using C-Mold®. The relationships between cooling time reduction (using aluminum alloys) and polymer type, cooling channel depth, part wall thickness, and coolant temperature were explored both experimentally and using simulation software. It was shown that the potential reduction in cooling time varied from 5% to 25%. The most significant percentage improvements were observed in parts with part wall thickness of 0.05″ to 0.10″ and in molds with cooling channels at a depth ratio (D/d) of 2.0. The thermal pulses in the steel mold 0.10″ from the surface were approximately 63% larger than in aluminum mold.

Optimization of fiber prediction model coefficients in injection molding simulation based on micro computed tomography

2018 ◽  
Vol 59 (s2) ◽  
pp. E152-E160 ◽  
Author(s):  
Matthias Morak ◽  
Daniel Tscharnuter ◽  
Thomas Lucyshyn ◽  
Wolfram Hahn ◽  
Michael Göttlinger ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Injection Molding ◽  
Prediction Model ◽  
Micro Computed Tomography ◽  
Simulation Based ◽  
Injection Molding Simulation

Dynamic Modeling and Control of Packing Pressure in Injection Molding

1994 ◽  
Vol 116 (2) ◽  
pp. 244-249 ◽  
Author(s):  
J. Hu ◽  
J. H. Vogel
Keyword(s):  
Injection Molding ◽  
Closed Loop ◽  
Good Control ◽  
Pressure Control ◽  
Physical Parameters ◽  
Closed Loop System ◽  
Modeling And Control ◽  
Packing Pressure ◽  
And Control ◽  
Plastic Injection

A dynamic model of injection molding developed from physical considerations is used to select PID gains for pressure control during the packing phase of thermo-plastic injection molding. The relative importance of various aspects of the model and values for particular physical parameters were identified experimentally. The controller gains were chosen by pole-zero cancellation and root-locus methods, resulting in good control performance. Both open and closed-loop system responses were predicted and verified, with good overall agreement.

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