Experimental analysis and simulation on injection molding melt temperature

1998 ◽  
Author(s):  
Chunhua Zhao
Keyword(s):  
Injection Molding ◽  
Experimental Analysis ◽  
Melt Temperature

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

Determination of Optimum Process Parameters in Gas-Assisted Injection Molding

2011 ◽  
Vol 143-144 ◽  
pp. 494-498
Author(s):  
Ke Ming Zi ◽  
Li Heng Chen
Keyword(s):  
Injection Molding ◽  
Delay Time ◽  
Optimization Design ◽  
Gas Pressure ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Element Analysis ◽  
Melt Temperature

With finite element analysis software Moldflow, numerical simulation and studies about FM truck roof handle were conducted on gas-assisted injection molding process. The influences of melt pre-injection shot, gas pressure, delay time and melt temperature were observed by using multi-factor orthogonal experimental method. According to the analysis of the factors' impact on evaluation index, the optimized parameter combination is obtained. Therefore the optimization design of technological parameters is done. The results show that during the gas-assisted injection molding, optimum pre-injection shot is 94%,gas pressure is 15MPa,delay time is 0.5s,melt temperature is 240 oC. This study provided a more practical approach for the gas-assisted injection molding process optimization.

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.

scholarly journals Effects of Injection Molding Screw Tips on Polymer Mixing

2018 ◽  
Vol 62 (3) ◽  
pp. 241-246 ◽  
Author(s):  
Dániel Török ◽  
József Gábor Kovács
Keyword(s):  
Injection Molding ◽  
Processing Parameters ◽  
Raw Material ◽  
Molding Machine ◽  
Surface Color ◽  
Melt Temperature ◽  
Injection Molding Machine ◽  
Aesthetic Appearance ◽  
Molded Parts ◽  
Injection Molded

In all fields of industry it is important to produce parts with good quality. Injection molded parts usually have to meet strict requirements technically and aesthetically. The aim of the measurements presented in our paper is to investigate the aesthetic appearance, such as surface color homogeneity, of injection molded parts. It depends on several factors, the raw material, the colorants, the injection molding machine and the processing parameters. In this project we investigated the effects of the injection molding machine on surface color homogeneity. We focused on injection molding screw tips and investigated five screw tips with different geometries. We produced flat specimens colored with a masterbatch and investigated color homogeneity. To evaluate the color homogeneity of the specimens, we used digital image analysis software developed by us. After that we measured the plastication rate and the melt temperature of the polymer melt because mixing depends on these factors. Our results showed that the screw tips (dynamic mixers) can improve surface color homogeneity but they cause an increase in melt temperature and a decrease in the plastication rate.

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.

Effects of process parameters on the formability of sheet metal during plastic injection forming

2020 ◽  
Vol 62 (5) ◽  
pp. 535-543
Author(s):  
Mirigul Altan ◽  
Bora Sener ◽  
Mirigul Altan
Keyword(s):  
Injection Molding ◽  
Sheet Metal ◽  
Injection Pressure ◽  
Industrial Applications ◽  
Effective Parameter ◽  
Melt Temperature ◽  
Injection Speed ◽  
Aluminum Sheets ◽  
Plastic Injection ◽  
Thinning Rate

Abstract Plastic injection forming (PIF) is an alternative sheet metal forming method for complex geometrical parts with dimensions in low tolerance. This method is a combination of injection molding and hydroforming in which a molten polymer material has been injected over a sheet metal via a plastic injection molding machine. In this study, aluminum sheets 1.5 mm thick were shaped by PIF at various injection pressures, melt temperatures and injection speed. The effects of these parameters on the formability of the sheet metal were investigated using the experimental design technique. The thinning rate, flange radius and the hardness values of the shaped sheets were considered in the experimental study. Injection pressure was found to be the most effective parameter and melt temperature was the second degree effective parameter for the thinning rate. The usability of the PIF process in industrial applications as an alternative method was emphasized by comparing PIF with conventional hydroforming by means of the finite element method (Ls-Dyna). A 2.07 % deviation was observed between the FE results for hydroforming and the experimental results for PIF.

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