Optimization of Injection Molding Parameters for Plastic Part Using Taguchi-TOPSIS Method

2011 ◽  
Vol 421 ◽  
pp. 440-443 ◽  
Author(s):  
Shie Chen Yang ◽  
Feng Che Tsai ◽  
Tsuo Fei Mao ◽  
Amine Ghali Benna ◽  
Ling You Huang
Keyword(s):  
Injection Molding ◽  
Volumetric Shrinkage ◽  
Prototype Model ◽  
Topsis Method ◽  
Plastic Part ◽  
Melt Temperature ◽  
Automobile Part ◽  
Finite Element Software ◽  
Injection Parameters ◽  
Outstanding Result

This paper reports an simulation study to improve the shrinkage of plastic automobile part by optimizing the injection molding parameters using the Taguchi-TOPSIS method. The Reverse Engineering(RE) and rapid prototyping (RP) technique were used to modelling and createing a prototype model of plastic automobile part. Employing the finite element software, MoldFlow Plastic Insight, the effect of injection parameters on the warpage and shrinkage of plastic part were examined carefully. The simulation results show that Taguchi-TOPSIS method provided an outstanding result for the optimization of injection parameters to produce minimum volumetric shrinkage. The injection parameter of melt temperature was the most significant parameter that influences the warpage and volumetric shrinkage for plastic automobile part in this study.

Analysis of Volumetric Shrinkage and Optimization of Process Parameters in Injection Molding

2012 ◽  
Vol 217-219 ◽  
pp. 2065-2069 ◽  
Author(s):  
Ji Ping Chen ◽  
Zhi Ping Ding
Keyword(s):  
Injection Molding ◽  
Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Processing Parameters ◽  
Volumetric Shrinkage ◽  
Plastic Part ◽  
Melt Temperature ◽  
Optimization Of Process Parameters ◽  
Design Variables

Based on experimental method of factorial design, numerical simulation of injection molding using Moldflow software has been done. The effects of the gate number-distribution, melt temperature, packing pressure, packing time, injection time, etc. on volumetric shrinkage and the two-way interactions among factors in injection molding of plastic part have been researched. The regression model for volumetric shrinkage of plastic part has been obtained through multiple linear regression analysis of the experimental data. The optimization model has been established to optimize processing parameters setting leading factors which effect volumetric shrinkage as design variables and regression model of volumetric shrinkage as objective function. The volumetric shrinkage value of plastic part is less than the minimum volumetric shrinkage value of the main experiment, which indicates that the method has a good practical value in engineering.

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

The simulation of the warpage rule of the thin-walled part of polypropylene composite based on the coupling effect of mold deformation and injection molding process

2018 ◽  
Vol 25 (3) ◽  
pp. 593-601 ◽  
Author(s):  
Jixiang Zhang ◽  
Xiaoyi Yin ◽  
Fengzhi Liu ◽  
Pan Yang
Keyword(s):  
Injection Molding ◽  
Coupling Effect ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Plastic Part ◽  
Molding Process ◽  
Melt Temperature ◽  
Packing Pressure ◽  
Thin Walled ◽  
Packing Time

Abstract Aiming at the problem that a thin-walled plastic part easily produces warpage, an orthogonal experimental method was used for multiparameter coupling analysis, with mold structure parameters and injection molding process parameters considered synthetically. The plastic part deformation under different experiment schemes was comparatively studied, and the key factors affecting the plastic part warpage were analyzed. Then the injection molding process was optimized. The results showed that the important order of the influence factors for the plastic part warpage was packing pressure, packing time, cooling plan, mold temperature, and melt temperature. Among them, packing pressure was the most significant factor. The optimal injection molding process schemes reducing the plastic part warpage were melt temperature (260°C), mold temperature (60°C), packing pressure (150 MPa), packing time (2 s), and cooling plan 3. In this situation, the forming plate flatness was better.

scholarly journals ANALYSIS ON VOLUMETRIC SHRINKAGE OF PLASTIC FOOD CONTAINER MADE FROM AN INJECTION MOLDING PROCESS

2020 ◽  
Vol 8 (2) ◽  
pp. 22-31
Author(s):  
Rosidah Jaafar ◽  
◽  
Hambali Arep ◽  
Effendi Mohamad ◽  
Jeefferie Abd Razak ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Food Packaging ◽  
Plastic Injection Molding ◽  
Volumetric Shrinkage ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Plastic Injection

The plastic injection molding process is one of the widely used of the manufacturing process to manufacture the plastic product with high productivity. Moreover, the food packaging manufacturing industry undergoes the trials and errors to obtain the optimal setting of the process parameters in order to minimize the quality issues and these trials and errors are time consuming and costly. The aim of this study is to improve the quality of the butter tub by minimizing the volumetric shrinkage. This study is to deal with the application of Moldflow integrating with the statistical technique to minimize the volumetric shrinkage the butter tub which depends on the process parameters of the plastic injection molding. For this purpose, the rectangular shape of butter tub is designed by utilizing the SolidWorks. Molflow is used to simulate the plastic filling of the single cavity mold of butter tub based on the Taguchi’s �!" orthogonal array table. In addition, the analysis of variance (ANOVA) is applied to investigate significant impact of the process parameters on the quality of the butter tub. Minitab is used to optimize the response of the volumetric shrinkage by selecting the most appropriate process parameters that maximizing the desirability value. Furthermore, the butter tub has a uniform thickness which was 1.2 mm and its factor of safety was 3.383 and the volumetric shrinkage response have optimized by 0.956 %. The melt temperature and mold temperature are found to be the most significant process parameters for the plastic injection molding process of butter tub and the volumetric shrinkage value obtained from the simulation is verified by the calculated volumetric shrinkage value.

scholarly journals Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Saad M. S. Mukras
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Cycle Time ◽  
Optimization Problem ◽  
Extreme Values ◽  
Volumetric Shrinkage ◽  
Injection Molding Process ◽  
Product Cycle ◽  
Molding Process ◽  
Melt Temperature

This paper presents a framework for optimizing injection molding process parameters for minimum product cycle time subjected to constraints on the product defects. Two product defects, namely, volumetric shrinkage and warpage, as well as seven process parameters including injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature, were considered. Injection molding experiments were conducted on specifically chosen test points and results were used to compute the volumetric shrinkage and warpage (at each test point). Thereafter, three relationships between the product cycle time (one relationship), the two product defects (two relationships), and the injection molding parameters were constructed using the kriging technique. An optimization problem to minimize the product cycle time (described by the first relationship) subject to constraints on the product defects (described by the latter two relationships) was then formulated. A combination set of points between the lower and upper extreme values of acceptable product defect was generated to serve as constraints for the two product defects. The optimization problem was then solved using the Fmincon function, available in the Matlab optimization toolbox. A plot of the optimization results revealed an appreciable tradeoff between the cycle time and the two product defects. To validate the optimization, an additional injection molding experiment was conducted for one of the optimization results. Results from the additional experiment showed reasonably close agreement with simulation optimization results differing in the cycle time, the warpage and volumetric shrinkage by 6.7%, 3.2%, and 8%, respectively.

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.

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