Injection Molding Process Optimization of Multi-Objective Based on MUD-RBFNN-GA

2010 ◽  
Vol 37-38 ◽  
pp. 564-569
Author(s):  
Bao Shou Sun ◽  
Zhe Chen ◽  
Bo Qin Gu ◽  
Xiao Diao Huang
Keyword(s):  
Injection Molding ◽  
Process Optimization ◽  
Simulation Analysis ◽  
Weld Line ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Multi Objective ◽  
Uniform Designs ◽  
Moldflow Simulation

The optimization algorithm of MUD-RBFNN-GA was proposed in this article. An injection molding process optimization model of multi-factor and multi-objective was also researched. The multiple uniform designs of experiment was applied to optimize the processing parameters. During this process, the RBF neural network was established, where the melt temperature, mold temperature and packing pressure were taken as the inputs, and warpage, area of air-traps and weld-line length as the outputs, and the Moldflow simulation analysis was used to obtain the output values. By combining the algorithm with genetic algorithm and global optimization in the networks, we can get the optimal process parameters. The results show that the multi-objective optimization based on MUD-RBFNN-GA is practically applicable, and it can reduce the molding defects effectively.

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

Injection Molding Process Parameter Optimization for Warpage Minimization Based on Uniform Design of Experiment

2010 ◽  
Vol 37-38 ◽  
pp. 570-575 ◽  
Author(s):  
Bao Shou Sun ◽  
Zhe Chen ◽  
Bo Qin Gu ◽  
Xiao Diao Huang
Keyword(s):  
Injection Molding ◽  
Design Of Experiment ◽  
Simulation Analysis ◽  
Uniform Design ◽  
Mold Temperature ◽  
Optimization Method ◽  
Processing Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature

To optimize injection molding warpage, this paper applies the uniform design of experiment method to search for the optimal injection molding processing parameters. The warpage. simulation analysis is accomplished by emplying Moldflow software. The melt temperature, mold temperature, injection time and packing pressure are regarded as processing parameters, and processing parameters are optimized through establishing a regression equation, and the optimization result and influence factors are analyzed. The results show that uniform design of experiment can reduce number of experiments used effectively and the quality of the product is greatly improved by the optimization method.

Simulation Analysis of Residual Stress of the Plastic Gear Based on Moldflow

2012 ◽  
Vol 501 ◽  
pp. 339-343 ◽  
Author(s):  
Meng Li ◽  
Hui Min Zhang ◽  
Yong Nie
Keyword(s):  
Residual Stress ◽  
Injection Molding ◽  
Process Parameters ◽  
Simulation Analysis ◽  
Gear Tooth ◽  
Orthogonal Experiment ◽  
Injection Molding Process ◽  
Tooth Root ◽  
Molding Process ◽  
Melt Temperature

Through the orthogonal experiment with Moldflow software, numerical simulation was conducted in different injection molding process parameters. The influence on the plastic gear tooth root residual stress from the mold temperature, melt temperature, injection time, packing time, and packing pressure was explored was explored. The results showed that: The selected process parameters for plastic gear tooth root of the residual stress effects in varying degrees. By optimizing the injection molding process parameters, the residual stress of injection was reduced to improve the quality of injection molding gear.

Expert System of Precise Injection Molding Process Optimization Based on Moldflow Software

2009 ◽  
Vol 87-88 ◽  
pp. 451-455
Author(s):  
Peng Cheng Xie ◽  
Bin Du ◽  
Zhi Yun Yan ◽  
Yu Mei Ding ◽  
Wei Min Yang
Keyword(s):  
Expert System ◽  
Injection Molding ◽  
Process Optimization ◽  
Weld Line ◽  
Injection Molding Process ◽  
Molding Process ◽  
Set Up ◽  
And Control

An expert system of precise injection molding process optimization based on Moldflow software was set up in this paper. Expert system of precise injection molding process optimization based on Moldflow-MPI module consist of optimization of packing curve, analyzer of parallelism and coaxiality, analyzer of process optimization and integrative forecaster of weld line. The system can be used in the process optimization of precise injection molding, the forecast and control of product properties, and the flaw elimination of product molding.

Multi-Objective Optimization in Manufacturing Engineering for Slender Pen Rod Injection Molding Quality Based on Grey Correlation

2013 ◽  
Vol 345 ◽  
pp. 486-493
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Grey Relational Analysis ◽  
Injection Molding Process ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Melt Temperature ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper a new approach for the optimization of the multi-objective injection molding process based on the Taguchi robust design combined with the grey relational analysis has been studied. A grey relational grade obtained from the multi-objective grey relational analysis is used to solve the injection molding process with the multiple performance characteristics including volume shrinkage (R1) and axial deformation (R2), the injecting parameters, namely mold temperature, melt temperature, holding pressure and holding time are optimized. By orthogonal polar difference analysis and statistical analysis of variance (ANOVA) of grey relational grade, main factors influencing and the best process parameters were determined: A=50°C,B=250°C,C=30MPa,D=9s.Under the case of continuity factor, Fitting the response surface further the optimal combination of in continuous space r is identified: A=50.3°C,B=250°C,C=29MPa,D=8.3s. Experimental results have shown that the Taguchi combined with the grey relational analysis can avoid human evaluation of the multi-objective optimization, and Injection molding multi-objective optimization is implemented more objectively, and product performance in the process can be improved effectively through this approach.

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.

Using Taguchi Method to Optimize Molding Process Parameters of Chair Base

2012 ◽  
Vol 271-272 ◽  
pp. 1190-1194
Author(s):  
Hsueh Lin Wu ◽  
Ya Hui Wang
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
Simulation Analysis ◽  
Mold Temperature ◽  
Cooling Time ◽  
Injection Time ◽  
Molding Process ◽  
Melt Temperature ◽  
Packing Pressure ◽  
Moldflow Simulation

In this study, volumetric shrinkage at ejection of the chair base in the injection process, application of the 3D CAD software pro/e to design the shape of the product, and then combines moldflow simulation analysis and Taguchi method with L25 Orthogonal Array to determine the optimal injection molding parameters combination. In the Taguchi L25 experimental design, the six controlling factors used are melt temperature, mold temperature, injection time, packing time, packing pressure and cooling time, the result of experiment revealed that the optimum combination of parameters was the A2 (melting temperature 265°C), B3 (mold temperature 40°C), C2 (injection time 1.7sec), D4 (packing pressure 95%), E5 (packing time20sec), F5 (cooling time 20sec). The results show that the combination of Taguchi method and Moldflow can not only improve the molding process parameters effectively, but also optimize the quality of the products.

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