Thin-Wall Plastic Parts’ Warpage Analysis Based on Taguchi Method

2011 ◽  
Vol 80-81 ◽  
pp. 375-378
Author(s):  
Jian Zhong Chu ◽  
Rong Song
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Test ◽  
Thin Wall ◽  
Engineering Technology ◽  
Important Indicator ◽  
Leading Role ◽  
Optimization Of Process Parameters ◽  
Wall Injection ◽  
Plastic Parts

Warping deformation is an important indicator to evaluate the performance of thin-wall Plastic. In this paper, using the CAE（computer aided engineering）technology and DOE（Design of experiment）in the thin-wall injection molding field, take a box-shaped thin-wall plastic parts for example, using the moldflow software to simulation analyze of the process parameters of injection molding. By analyzing the causes of plastic parts’ warpage, learn the holding pressure is a leading role in warping. Optimization of process parameters under the guidance of the orthogonal test, it can reduce the warpage of plastic parts effectively.

Investigation of mold plate separation in thin-wall injection molding

2003 ◽  
Vol 22 (4) ◽  
pp. 306-319 ◽  
Author(s):  
Shia-Chung Chen ◽  
Wei-Liang Liaw ◽  
Pao-Lin Su ◽  
Ming-Hsiu Chung
Keyword(s):  
Injection Molding ◽  
Thin Wall ◽  
Mold Plate ◽  
Plate Separation ◽  
Wall Injection

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.

Anisotropic multilayer conductive networks in carbon nanotubes filled polyethylene/polypropylene blends obtained through high speed thin wall injection molding

Polymer ◽  
2013 ◽  
Vol 54 (23) ◽  
pp. 6425-6436 ◽  
Author(s):  
Feilong Yu ◽  
Hua Deng ◽  
Qin Zhang ◽  
Ke Wang ◽  
Chaoliang Zhang ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Injection Molding ◽  
High Speed ◽  
Thin Wall ◽  
Wall Injection ◽  
Polypropylene Blends

Analysis of Residual Stress in Thin Wall Injection Molding

2006 ◽  
Vol 306-308 ◽  
pp. 1331-1336
Author(s):  
H.K. Lee ◽  
J.C. Huang ◽  
G.E. Yang ◽  
Hong Gun Kim
Keyword(s):  
Residual Stress ◽  
Stress Distribution ◽  
Injection Molding ◽  
Molecular Orientation ◽  
Polymeric Materials ◽  
Residual Stress Distribution ◽  
Thin Wall ◽  
Flow Paths ◽  
Wall Injection ◽  
Relationship Of

A relationship of residual stress distribution and surface molding states on polymeric materials is presented in thin-walled injection molding. The residual stress is computed by computational numerical analysis, observed with stress viewer and birefringence. The residual stress on polymeric parts can allude the surface quality as well as flow paths. The residual stress distribution on polymeric parts is related with thickness, gate layout, and polymer types. Molecular orientation on polymeric parts is also important in thin wall injection molding. The residual stress and molecular orientation are related to the surface molding states intimately. Analysis of the residual stress is validated through photo-elastic method and surface molding states..

scholarly journals The Feasibility of an Internal Gas-Assisted Heating Method for Improving the Melt Filling Ability of Polyamide 6 Thermoplastic Composites in a Thin Wall Injection Molding Process

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1004 ◽  
Author(s):  
Thanh Trung Do ◽  
Tran Minh The Uyen ◽  
Pham Son Minh
Keyword(s):  
Injection Molding ◽  
Heating Rate ◽  
Temperature Control ◽  
Cavity Surface ◽  
Heating Process ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Molding Process ◽  
Filling Ability ◽  
Wall Injection

In thin wall injection molding, the filling of plastic material into the cavity will be restricted by the frozen layer due to the quick cooling of the hot melt when it contacts with the lower temperature surface of the cavity. This problem is heightened in composite material, which has a higher viscosity than pure plastic. In this paper, to reduce the frozen layer as well as improve the filling ability of polyamide 6 reinforced with 30 wt.% glass fiber (PA6/GF30%) in the thin wall injection molding process, a preheating step with the internal gas heating method was applied to heat the cavity surface to a high temperature, and then, the filling step was commenced. In this study, the filling ability of PA6/GF30% was studied with a melt flow thickness varying from 0.1 to 0.5 mm. To improve the filling ability, the mold temperature control technique was applied. In this study, an internal gas-assisted mold temperature control (In-GMTC) using different levels of mold insert thickness and gas temperatures to achieve rapid mold surface temperature control was established. The heating process was observed using an infrared camera and estimated by the temperature distribution and the heating rate. Then, the In-GMTC was employed to produce a thin product by an injection molding process with the In-GMTC system. The simulation results show that with agas temperature of 300 °C, the cavity surface could be heated under a heating rate that varied from 23.5 to 24.5 °C/s in the first 2 s. Then, the heating rate decreased. After the heating process was completed, the cavity temperature was varied from 83.8 to about 164.5 °C. In-GMTC was also used for the injection molding process with a part thickness that varied from 0.1 to 0.5 mm. The results show that with In-GMTC, the filling ability of composite material clearly increased from 2.8 to 18.6 mm with a flow thickness of 0.1 mm.

Sign in / Sign up

Export Citation Format

Share Document