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Journal articles on the topic 'Injection molding simulation'

Author: Grafiati
Published: 28 June 2021
Last updated: 14 February 2022

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1

NAKANO, Ryo. "Injection Molding Simulation." Journal of the Japan Society for Technology of Plasticity 47, no. 543 (2006): 273–78. http://dx.doi.org/10.9773/sosei.47.273.

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2

NAKANO, Ryo. "Injection Molding CAE Simulation." Journal of the Japan Society for Technology of Plasticity 50, no. 579 (2009): 296–300. http://dx.doi.org/10.9773/sosei.50.296.

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3

Goto, Terumasa. "Simulation of Injection Molding." Seikei-Kakou 2, no. 1 (1990): 45–51. http://dx.doi.org/10.4325/seikeikakou.2.45.

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4

Wei, Xiao Hua, and Bai Yang Lou. "Numerical Simulation Research of Micro-Injection Molding Simulation." Applied Mechanics and Materials 55-57 (May 2011): 1511–17. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1511.

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According to the basic theory and process of conventional injection molding, using the CAE software, numerical simulation research of the injection molding characteristic for micro thin-wall plastic parts are put forward. The effects of process parameters (melt temperature, mold temperature, injection pressure, injection rate) on molding characteristic of micro thin-wall plastic parts are discussed by single factor method, compare the significance of each factors.The simulation results showed that volume could be improved with the increase of melt temperature ,molding temperature, injection pressure and injection rate.
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5

Hourng, Lih-Wu, and Yau Si Lin. "Numerical Simulation of Debinding Process in Metal Injection Molding." International Journal of Modeling and Optimization 4, no. 6 (December 2014): 421–25. http://dx.doi.org/10.7763/ijmo.2014.v4.411.

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6

MIZUKAMI, AKIRA. "Injection molding polymeric flow simulation." NIPPON GOMU KYOKAISHI 63, no. 12 (1990): 727–30. http://dx.doi.org/10.2324/gomu.63.727.

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7

Satin, Lukáš, and Jozef Bílik. "Verification CAE System for Plastic Injection." Applied Mechanics and Materials 834 (April 2016): 79–83. http://dx.doi.org/10.4028/www.scientific.net/amm.834.79.

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This article is focused on the field of computer simulation and it is subsequent verification in practice. The work highlights the injection process, the simulation software that is specialized in injection molding and the technology process of injection itself. The major subject of the thesis is the use of the computer aided injection molding technology by using the CAE systems. The experimental part of the thesis deals with the production of the 3D model specific plastic parts in two modifications, injection molding simulation in the system Moldex3D and digitization of moldings on the optical 3D scanner. In the thesis we also provide measuring realization on digitized models and comparison of the parts size with the computer model. In conclusion we summarize the results achieved from the comparison. The thesis is carried out in cooperation with the Simulpast s.r.o.
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8

Miao, Li Lei, Peng Cheng Xie, Pan Pan Zhang, and Wei Min Yang. "Numerical Simulation of Differential Injection Molding Based on Moldex 3D." Key Engineering Materials 501 (January 2012): 225–30. http://dx.doi.org/10.4028/www.scientific.net/kem.501.225.

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Differential injection molding is one of the key technologies for micro-manufacture. The mechanism of melt pump based on the differential injection molding is presented. The differential injection molding system is added into the traditional injection machines. Melt is plasticized by the plastication system, then after being split, pressurized and measured by the differential system, it enters the cavity to realize the function of multiple micro injection molding machines. Moldex 3D software is used to simulate the filling history of the micro-structure via differential injection molding technology. The product's filling volume under different processing parameters is compared, which provides theoretical basis for the optimization of the molding parameters in differential injection molding technology.
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9

Zink, Béla, Ferenc Szabó, István Hatos, András Suplicz, Norbert Kovács, Hajnalka Hargitai, Tamás Tábi, and József Kovács. "Enhanced Injection Molding Simulation of Advanced Injection Molds." Polymers 9, no. 12 (February 22, 2017): 77. http://dx.doi.org/10.3390/polym9020077.

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10

Matsuoka, Takaaki. "Computer Simulation in Thermoplastic Injection Molding." Nihon Reoroji Gakkaishi(Journal of the Society of Rheology, Japan) 23, no. 4 (1995): 207–16. http://dx.doi.org/10.1678/rheology1973.23.4_207.

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11

Pidria, M., A. Pipino, D. Vallauri, G. Maizza, and I. Amato. "Simulation Practice of Powder Injection Molding." Advanced Engineering Materials 3, no. 4 (April 2001): 253–58. http://dx.doi.org/10.1002/1527-2648(200104)3:4<253::aid-adem253>3.0.co;2-5.

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12

Zhou, Huamin, Guodong Xi, and Fen Liu. "Residual Stress Simulation of Injection Molding." Journal of Materials Engineering and Performance 17, no. 3 (August 8, 2007): 422–27. http://dx.doi.org/10.1007/s11665-007-9156-6.

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13

Tzetzis, Dimitrios, Ioannis Sofianidis, and Panagiotis Kyratsis. "Injection Molding Simulation for Part Manufacture with Polystyrene." Applied Mechanics and Materials 809-810 (November 2015): 229–34. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.229.

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Being able to predict potential defects is a very important aspect of the manufacturing process. The simulation of the injection molding process enables mold and part designers to perform tests during design and development stage and therefore results in a more cost effective and efficient production cycle. This study examines the injection molding simulation of a polystyrene plastic part. The simulation analyzes how melted plastic flows through a mold in order to identify manufacturing defects, while it offers a wide range of simulation parameters and optimizations. Injection molding of the plastic part with the processing guidelines given from the simulation procedure proved successful in the actual manufacturing process. In particular, a significant number of simulations were conducted so that their effect to the final product could be identified. Applying different parameters and optimizations led to several defective parts and identified problems into the process. Common injection molding issues like the short shot occurrence, determining the best injection location or studying the pressure at end of packing had to be addressed by adjusting the simulation parameters and analyzing the results produced. After the procedure's completion it became obvious that the initial processing parameters had to be optimized as otherwise would result in extra manufacturing costs and in delays of production.
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14

Lin, Zheng Ying, and Wan Li Hou. "Optimization Design for Automotive Interior Trimmings Based on Moldflow." Key Engineering Materials 522 (August 2012): 515–19. http://dx.doi.org/10.4028/www.scientific.net/kem.522.515.

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Took the auxiliary fascia console lid of automotive interior trimmings for example, the preliminary plastic was designed by Pro/Engineer, then the Moldflow analysis on the plastic was performed. Those irrational factors of both injection technical parameters and structure of the plastic, which would occur in the injection molding process, were modified after analyzing simulation results. The rational plastic was obtained by repeating simulation and optimization, which indicated that the simulation-based optimization could improve the plastic quality and efficiency of injection molding. Meanwhile, the comparisons of mode locking force and molding time between microcellular foam and non-foam injection molding were also carried out, which showed the significance of numerical value simulation in injection molding processes.
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15

Isayev, A. I., and Toru Hosaki. "Temperature in Rubber Moldings during Injection Molding Cycle: Simulation and Experimentation." Journal of Elastomers & Plastics 23, no. 3 (July 1991): 176–91. http://dx.doi.org/10.1177/009524439102300303.

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16

Baldi-Boleda, Tomás, Ehsan Sadeghi, Carles Colominas, and Andrés García-Granada. "Simulation Approach for Hydrophobicity Replication via Injection Molding." Polymers 13, no. 13 (June 23, 2021): 2069. http://dx.doi.org/10.3390/polym13132069.

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Nanopattern replication of complex structures by plastic injection is a challenge that requires simulations to define the right processing parameters. Previous work managed to simulate replication for single cavities in 2D and 3D, showing high performance requirements of CPU to simulate periodic trenches in 2D. This paper presents two ways to approach the simulation of replication of complex 3D hydrophobic surfaces. The first approach is based on previous CFD Ansys Fluent and compared to FE based CFD Polyflow software for the analysis of laminar flows typical in polymer processing and glass forming as well as other applications. The results showed that Polyflow was able to reduce computing time from 72 h to only 5 min as desired in the project. Furthermore, simulations carried out with Polyflow showed that higher injection and mold temperature lead to better replication of hydrophobicity in agreement with the experiments. Polyflow simulations are proved to be a good tool to define process parameters such as temperature and cycle times for nanopattern replication.
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17

Bataineh, Omar M., and Barney E. Klamecki. "Prediction of Local Part-Mold and Ejection Force in Injection Molding." Journal of Manufacturing Science and Engineering 127, no. 3 (December 1, 2004): 598–604. http://dx.doi.org/10.1115/1.1951785.

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A numerical simulation system was developed to predict local part-mold forces and local and total ejection forces in injection molding. Local reaction forces between the part and mold surfaces are calculated first using numerical molding process and structural simulations. Using experimentally obtained coefficients of friction the friction force and ejection force are calculated. Ring moldings were used to measure the coefficient of friction. Box moldings were used to validate predictions of local and total ejection forces and to demonstrate the use of the system in mold design. Calculated ejection force was maximum at the beginning of ejection and differed by 10%–16% from experimental values, with the difference being much less over the main part of the ejection process. The maximum number of ejector pins for failed ejection was predicted. The difference between the predicted and observed number of ejector pins was at most four pins for a twenty ejector pin system.
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18

ITO, Hiroshi, Yasuhiro TSUTSUMI, Keiji MINAGAWA, Kazumi TADA, and Kiyohito KOYAMA. "A Simulation of Crystallization in Injection Molding." Seikei-Kakou 6, no. 4 (1994): 265–70. http://dx.doi.org/10.4325/seikeikakou.6.265.

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19

Johnston, Stephen, and David O. Kazmer. "Decoupled Gating and Simulation for Injection Molding." Polymer-Plastics Technology and Engineering 45, no. 4 (May 2006): 575–84. http://dx.doi.org/10.1080/03602550600555999.

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20

Brincat, Paul, Kapil Talwar, and Chris Friedl. "Extensional Viscosity Modelling for Injection Molding Simulation." Journal of Reinforced Plastics and Composites 18, no. 6 (April 1999): 499–507. http://dx.doi.org/10.1177/073168449901800602.

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21

Mannella, G. A., V. La Carrubba, V. Brucato, W. Zoetelief, and G. Haagh. "No-flow temperature in injection molding simulation." Journal of Applied Polymer Science 119, no. 6 (September 29, 2010): 3382–92. http://dx.doi.org/10.1002/app.32987.

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22

Ospald, Felix. "Numerical Simulation of Injection Molding using OpenFOAM." PAMM 14, no. 1 (December 2014): 673–74. http://dx.doi.org/10.1002/pamm.201410320.

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23

Tighe, S. C., and L. T. Manzione. "Simulation of foaming in reaction injection molding." Polymer Engineering and Science 28, no. 15 (August 1988): 949–54. http://dx.doi.org/10.1002/pen.760281503.

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24

Sönmez, D., and A. A. Eker. "Numerical Simulation and Process Optimization of a 3D Thin-Walled Polymeric Part Using Injection Compression Molding." International Polymer Processing 36, no. 4 (September 1, 2021): 459–67. http://dx.doi.org/10.1515/ipp-2020-4075.

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Abstract Injection compression molding (ICM) is a hybrid injection molding process for manufacturing polymer products with high precision and surface accuracy. In this study, a 3D flow simulation was employed for ICM and injection molding (IM) processes. Initially, the process parameters of IM and ICM were discussed based on the numerical simulations. The IM and ICM processes were compared via numerical simulation by using CAE tools of Moldflow software. The effect of process parameters of mold surface temperature, melting temperature, compression force and injection time on clamping force and pressure at the injection location of molded 3D BJ998MO Polypropylene (MFI 100) part was investigated by Taguchi analysis. In conclusion, it was found that the ICM has a relatively lower filling pressure than ICM, which results in reduced clamping force for producing a 3D thin-walled polymeric part.
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25

Narowski, Przemysław, and Krzysztof Wilczyński. "Polymer Injection Molding: Advanced Simulations or Tablet Computations." Challenges of Modern Technology 7, no. 4 (December 30, 2016): 3–5. http://dx.doi.org/10.5604/01.3001.0010.8782.

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Simulation of injection molding of polymeric materials is a common way of solving issues in plastic part and injection mold design. CAE software is getting more available and user-friendly, which sometimes leads to unreasonable use cases of these programs. The original, relatively simple tool has been introduced to validate runner systems in injection molds. It allows a fast identification of the most important design parameters of runner system. This tool does not require any support of FEM simulations, but results obtained from it have been successfully compared with injection molding simulations.
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26

Li, Meng, Hui Min Zhang, and Yong Nie. "Simulation Analysis of Residual Stress of the Plastic Gear Based on Moldflow." Key Engineering Materials 501 (January 2012): 339–43. http://dx.doi.org/10.4028/www.scientific.net/kem.501.339.

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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.
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27

Deng, J. S., and A. I. Isayev. "Injection Molding of Rubber Compounds: Experimentation and Simulation." Rubber Chemistry and Technology 64, no. 2 (May 1, 1991): 296–324. http://dx.doi.org/10.5254/1.3538560.

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Abstract Results of experimental and theoretical studies of injection molding of rubber compounds have been reported. Characterizations on the rheological properties and the vulcanization kinetics of rubber compounds were carried out by means of MPT and DSC, respectively. The models were employed to fit these experimental data. An attempt has been made in extending the proposed empirical kinetic model based on DSC data to similar curing data obtained by means of the MDR technique. The heat-transfer effect due to the large sample size used in MDR measurements has been found to have a small effect on the kinetic data. Due to the different principle of state-of-cure measurements in MDR and DSC, the model parameters of curing kinetics have been found to be different in these measurements. A two-dimensional flow simulation of generalized Newtonian fluids based on both finite-difference and finite-element methods has been performed. The pressure development at various positions along the flow path during the filling stage of the molds was obtained experimentally for various injection speeds. The predicted results on pressure development during cavity filling showed qualitative agreement with the experimental data. Possible reasons for the observed discrepancy in pressure traces are: neglect of local extra pressure losses (in the juncture sections), compressibility of rubber compounds, leakage (back-flow) of material during injection, and voids formation in the injection chamber. The study on the vulcanization behavior of rubber compounds during injection molding process has also been done. The proposed empirical kinetic and induction time models were able to satisfactorily predict the cure levels of molded rubber products. At the same time, the results predicted by employing nth order kinetics were found to be unsatisfactory. The contribution of nonisothermal induction time in calculating cure levels of the molded rubber products was found to be significant. The mechanical properties and anisotropy have been investigated for two rubber compounds. It is suggested that there exists a mold temperature at which the properties and cycle times are optimal, and the filler type shows a significant effect on the tensile modulus. The rubber moldings were found to be highly anisotropic in mechanical behaviors. The anisotropy could be reduced significantly at high injection speed due to the faster stress-relaxation process.
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28

Kuang, Tang Qing. "Study on one Dimensional Numerical Simulation in Filling Stage of Water-Assisted Injection Molding." Advanced Materials Research 179-180 (January 2011): 1193–98. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.1193.

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Water assisted injection molding is a pretty novel way to fabricate hollow or more complicated parts. Its molding window and process control are more critical and difficult since additional processing parameters are involved. A simulation model for the filling stage of a pipe cavity during short-shot water assisted injection molding was proposed. The finite element/finite difference/control volume methods were adopted for the numerical simulation. A numerical study, based on the single factor method, was conducted to characterize the effect of different processing parameters on the short shot water-assisted injection-molding of thermoplastic composites, including short-shot size, melt temperature, mold temperature, water temperature and water pressure. For the factors selected in the simulations, short-shot size was found to be the principal parameters affecting the water penetration length while melt temperature, mold temperature, water temperature, water pressure were found to have little effect on the penetration of water.
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29

Isayev, A. I., M. Sobhanie, and J. S. Deng. "Two-Dimensional Simulation of Injection Molding of Rubber Compounds." Rubber Chemistry and Technology 61, no. 5 (November 1, 1988): 906–37. http://dx.doi.org/10.5254/1.3536226.

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Abstract A general finite-element and finite-difference computer program has been developed for filling a thin cavity during injection molding of rubber compounds. This program employs a finite-element representation of the cavity in the planar dimensions and a finite-difference representation in the gapwise direction and is capable of accommodating any type of rheological equation and vulcanization model and incorporates both viscous heating and heat of vulcanization. A detailed example of an application of this program has been presented for filling a quarter-disk cavity with rubber compounds having a viscosity function obeying a modified Cross model and curing kinetics according to our recently proposed nonisothermal vulcanization model. The pressure, velocity, and temperature fields and melt-front locations have been calculated during the filling stage. The evaluation of state of cure distribution in the rubber moldings has been predicted in the postfilling stage. Comparisons of the prediction of the model with experimental data regarding pressure distribution, melt front position, and distribution of degree of cure in molded parts will be presented in the near future.
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30

Hu, Qiao Sheng, Feng Ni, and Jian Ping Lin. "Strain Analysis on Weld Zone of Tailor Welded Blanks in the Case of Welded Seam Cracking." Advanced Materials Research 154-155 (October 2010): 355–58. http://dx.doi.org/10.4028/www.scientific.net/amr.154-155.355.

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A simulation model for the filling of a tubular cavity during water assisted injection molding is proposed. The polymer melt and water are assumed to be incompressible and to follow a Hele-Shaw fluid behavior. The finite element/finite difference/control volume methods are adopted for numerical simulation of the melt front, pressure at injection location variation, water thickness fraction and bulk temperature about a curved pipe, the simulation results have good agreement with the results presented in the former experiment. In comparison with the simulation result of gas-assisted injection molding, water assisted injection molding can give parts with thinner and more uniform walls and more rapid cooling.
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31

Kuang, Tang Qing. "Study on the Flow Behavior in a Tubular Cavity during Water-Assisted Injection Molding." Advanced Materials Research 154-155 (October 2010): 359–62. http://dx.doi.org/10.4028/www.scientific.net/amr.154-155.359.

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A simulation model for the filling of a tubular cavity during water assisted injection molding is proposed. The polymer melt and water are assumed to be incompressible and to follow a Hele-Shaw fluid behavior. The finite element/finite difference/control volume methods are adopted for numerical simulation of the melt front, pressure at injection location variation, water thickness fraction and bulk temperature about a curved pipe, the simulation results have good agreement with the results presented in the former experiment. In comparison with the simulation result of gas-assisted injection molding, water assisted injection molding can give parts with thinner and more uniform walls and more rapid cooling.
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32

Li, W., Yang Fu Jin, Xun Lv, and C. H. Kua. "A Study on Computer Simulation of Plastic Lens Molding Process." Materials Science Forum 471-472 (December 2004): 490–93. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.490.

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In this paper, some molding process parameters such as injection time, packing time, packing pressure and process temperature etc. were optimized by the Computer Aided Engineering (CAE) simulation (Moldex 3D) for injection molding of a plastic lens. Some experimental trials were carried out for verifying of the CAE simulation results with checking of the lens shrinkage and birefringence etc. as well. The results showed that, the recommended molding process parameters from CAE simulation and the actual experiments were almost the same, hence the CAE is a established tool based on the scientific approach to reduce experimental works, to identify critical parameters and to save substantial costs. Lately, a perfect plastic lens was gained by the Injection–Compression Molding process with the optimized process parameters by a CAE simulation.
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33

Chen, Kai Yuan, Meng Xue Yan, Ying Wang, and Nan Qiao Zhou. "Injection Speed Control of Precision Injection Molding Based on CMAC Neural Network." Applied Mechanics and Materials 624 (August 2014): 444–48. http://dx.doi.org/10.4028/www.scientific.net/amm.624.444.

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Injection speed is one of the most important parameters during precision injection molding. The basic theory of CMAC neural network is introduced in this paper. The compound control algorithm based on CMAC and PID is applied to injection speed control of precision injection molding. The simulation shows the compound control algorithm has high control accuracy and excellent control result.
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34

Ellson, Richard, and Donna Cox. "Visualization of injection molding." SIMULATION 51, no. 5 (November 1988): 184–88. http://dx.doi.org/10.1177/003754978805100504.

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35

Deng, Lin, Junjie Liang, Yun Zhang, Huamin Zhou, and Zhigao Huang. "Efficient numerical simulation of injection mold filling with the lattice Boltzmann method." Engineering Computations 34, no. 2 (April 18, 2017): 307–29. http://dx.doi.org/10.1108/ec-01-2016-0023.

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Purpose Lattice Boltzmann method (LBM) has made great success in computational fluid dynamics, and this paper aims to establish an efficient simulation model for the polymer injection molding process using the LBM. The study aims to validate the capacity of the model for accurately predicting the injection molding process, to demonstrate the superior numerical efficiency in comparison with the current model based on the finite volume method (FVM). Design/methodology/approach The study adopts the stable multi-relaxation-time scheme of LBM to model the non-Newtonian polymer flow during the filling process. The volume of fluid method is naturally integrated to track the movement of the melt front. Additionally, a novel fractional-step thermal LBM is used to solve the convection-diffusion equation of the temperature field evolution, which is of high Peclet number. Through various simulation cases, the accuracy and stability of the present model are validated, and the higher numerical efficiency verified in comparison with the current FVM-based model. Findings The paper provides an efficient alternative to the current models in the simulation of polymer injection molding. Through the test cases, the model presented in this paper accurately predicts the filling process and successfully reproduces several characteristic phenomena of injection molding. Moreover, compared with the popular FVM-based models, the present model shows superior numerical efficiency, more fit for the future trend of parallel computing. Research limitations/implications Limited by the authors’ hardware resources, the programs of the present model and the FVM-based model are run on parallel up to 12 threads, which is adequate for most simulations of polymer injection molding. Through the tests, the present model has demonstrated the better numerical efficiency, and it is recommended for the researcher to investigate the parallel performance on even larger-scale parallel computing, with more threads. Originality/value To the authors’ knowledge, it is for the first time that the lattice Boltzmann method is applied in the simulation of injection molding, and the proposed model does obviously better in numerical efficiency than the current popular FVM-based models.
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36

Sahli, Mohamed, Jean Claude Gelin, and Thierry Barrière. "Simulation and Modeling of Sintering Process for 316L Stainless Steel Metal Injection Molding Parts." Key Engineering Materials 651-653 (July 2015): 32–37. http://dx.doi.org/10.4028/www.scientific.net/kem.651-653.32.

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The metal injection molding (MIM) process allows the manufacturing of small and very complex metallic components. The metal injection molding processing combines the shaping capability of polymer injection molding with the large material variety of metals and ceramics. This paper discusses in detail the development of a numerical model capable of simulating micro-structural evolution and macroscopic deformation during sintering of complex powder compacts. A sintering model based on elastic–viscoplastic constitutive equations was proposed and the corresponding parameters such as bulk viscosity, shearing viscosity and sintering stress were identified from dilatometer experimental data. The constitutive model was then implemented into finite element software in order to perform the simulation of the sintering process. The numerical simulation methods being compared against results of the sintering experiments. The experimental data were obtained from sintering of 316L stainless steel powders.
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37

Lam, Y. C., X. Chen, K. C. Tam, and S. C. M. Yu. "Simulation of Particle Migration of Powder-Resin System in Injection Molding." Journal of Manufacturing Science and Engineering 125, no. 3 (July 23, 2003): 538–47. http://dx.doi.org/10.1115/1.1580850.

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Powder injection molding is an important processing method for producing precision metallic or ceramic parts. Experience, intuition and trial-and-error have been the practice for the design and process optimization of such molding operations. However, this practice is becoming increasingly inefficient and impractical for the molding of larger, more complicated and more costly parts. In this investigation, a numerical method for simulating the mold-filling phase of powder injection molding was developed. The flow was modelled using the Hele-Shaw approach coupled with particle diffusion transport equation for the calculation of powder concentration distribution. The viscosity of the feedstock was evaluated using a power-law type rheological model to account for the viscosity dependency on shear rate and powder concentration. A numerical example is presented and discussed to demonstrate the capabilities and limitations of the simulation algorithm, which has the potential as an analytical tool for the mold designer. The variation of powder density distribution can be predicted, which is ignored by the existing simulation packages. Preliminary simulation indicated that powder concentration variation could be significant. Non-isothermal analysis indicated that most of the key parameters for filling process would change due to a change in powder concentration distribution.
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38

Ozawa, Taku. "3D Injection Molding Simulation Software Moldex 3D R11." Seikei-Kakou 24, no. 4 (March 20, 2012): 212. http://dx.doi.org/10.4325/seikeikakou.24.212.

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39

Yamamoto, Hironori. "Injection Molding Simulation System for the Concurrent Engineering." Seikei-Kakou 4, no. 4 (1992): 237–43. http://dx.doi.org/10.4325/seikeikakou.4.237.

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40

Ling, D., M. Gupta, P. R. Myers, and R. K. Upadhyay. "Simulation of Core Deflection in Powder Injection Molding." International Polymer Processing 21, no. 3 (July 2006): 309–18. http://dx.doi.org/10.3139/217.0105.

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41

Pina-Estany, J., and A. A. García-Granada. "3D Simulation of Nanostructures Replication via Injection Molding." International Polymer Processing 32, no. 4 (August 11, 2017): 483–88. http://dx.doi.org/10.3139/217.3383.

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42

YATABE, Hiroyuki. "The Injection Molding Simulation Towards the Product Performance." NIPPON GOMU KYOKAISHI 89, no. 12 (2016): 362–67. http://dx.doi.org/10.2324/gomu.89.362.

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43

Jiang, Shao Fei, Jia Bo Zhang, Guo Zhong Chai, Ji Quan Li, and Mao Ying Su. "Cooling Simulation of External Gas-Assisted Injection Molding." Applied Mechanics and Materials 44-47 (December 2010): 1607–11. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1607.

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This paper present a cooling simulation method for External Gas-assisted Injection Molding (EGAIM). To simulate the cooling process of EGAIM, a Finite Element Method model with heat transfer processing with isotropic materials containing inner heat source was established, and pressure was loaded on the interface of mold and part as gas packing pressure. The simulation was used to explore the effect of process parameters on the quality of an example with different thickness ribs with ANSYS. The results showed the method is feasible and reliable.
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44

Aluru, Rajitha, Michael Keefe, and Suresh Advani. "Simulation of injection molding into rapid‐prototyped molds." Rapid Prototyping Journal 7, no. 1 (March 2001): 42–51. http://dx.doi.org/10.1108/13552540110365153.

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45

Sahu, Rakesh, Donggang Yao, and Byung Kim. "Simulation of Filling Pattern of Multicomponent Injection Molding." Polymer-Plastics Technology and Engineering 38, no. 2 (April 1999): 241–54. http://dx.doi.org/10.1080/03602559909351574.

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46

Zhang, Shixun, Shaozhen Hua, Wei Cao, Zhiyu Min, Yongzhi Liu, Yaming Wang, and Changyu Shen. "3D Viscoelastic Simulation of Jetting in Injection Molding." Polymer Engineering & Science 59, s2 (February 8, 2019): E397—E405. http://dx.doi.org/10.1002/pen.25071.

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47

Johnston, Stephen P., David O. Kazmer, and Robert X. Gao. "Online simulation-based process control for injection molding." Polymer Engineering & Science 49, no. 12 (December 2009): 2482–91. http://dx.doi.org/10.1002/pen.21481.

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48

Vietri, U., A. Sorrentino, V. Speranza, and R. Pantani. "Improving the predictions of injection molding simulation software." Polymer Engineering & Science 51, no. 12 (July 25, 2011): 2542–51. http://dx.doi.org/10.1002/pen.22035.

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49

IWAI, TAKASHI, TATSUHIKO AIZAWA, and JUNJI KIHARA. "GRANULAR FLOW SIMULATION FOR METAL INJECTION MOLDING PROCESS." International Journal of Modern Physics B 07, no. 09n10 (April 20, 1993): 2047–56. http://dx.doi.org/10.1142/s0217979293002766.

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Metal Injection Molding treats the complex fluid which consists of thermoplastic tic polymer medium and dense metallic powder suspensions to improve flowability and formability. To understand its fundamental mechanical behavior, it is important to consider powder structures and mechanics precisely. For the analysis of this process, a new granular model is proposed, which is based on the Distinct. Element Method. Each element in this method is constituted by combining a metal powder with a binder (polymer) shell surrounding it. Both elasticity and viscosity for powder particles and binders are only considered in this mixture model as the constitutive relations. Several numerical results have demonstrated the effectiveness and validity of our developed granular modeling to deal with the various phenomena appearing in MIM process.
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50

Zhou, Huamin, and Dequn Li. "Numerical Molding Simulation for Rapid-Prototyped Injection Molds." Polymer-Plastics Technology and Engineering 44, no. 4 (May 2005): 755–70. http://dx.doi.org/10.1081/pte-200060376.

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