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Identyfikatory
ISBN
10.24425/mper.2024.151488
Warianty tytułu
Języki publikacji
Abstrakty
Purpose: to analyze the optimum parameters in the injection molding of carbon fiber 66 (PA66) composite resin obtained from the injection molding process of the electrical connector assembly. Design/methodology/approach: Experimental research will examine relevant parameters uti lizing experimental design based on the Genetic Algorithm Method. Findings The optimum parameters in the injection molding process are Injection Pressure at 110 MPa, Holding Pressure at 55 MPa, Holding Time at 1.0 sec, Injection Speed at 40 mm/s, Injection Stroke at value 15 mm, which will make the results of the injection workpiece pro duced from the injection machine be the length that is in the standard value. Practical implications: The findings improved the molding problem: Short shot problem enhanced by 1.15%; Weld line problem improved by 0.38%; Silver mark problem enhanced by 0.11%. Originality/value: Decrease the defect from a short shot problem in the injection molding process to save time and reduce production costs.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1--11
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr., zdj.
Twórcy
autor
- Rajamangala University of Technology Krungthep, Department of Industrial Engineering, Thailand
autor
- Rajamangala University of Technology Krungthep, Department of Industrial Engineering, Thailand
autor
- Rajamangala University of Technology Krungthep, Thailand 10120
Bibliografia
- Blanco, A., Delgado, M., & Pegalajar, M.C. (2001). A real-coded genetic algorithm for training recurrent neural networks. Neural Networks, 14 (1), 93–105.
- Chow, T.T., Zhang, G.Q., Lin, Z., & Song, C.L. (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy Build., 34 (1), 103–109.
- Cook, D.F., Ragsdale, C.T., & Major, R.L. (2000). Combining a neural network with a genetic algorithm for process parameter optimization. Eng. Applica. Artif. Intel., 13 (4), 391–396.
- Choudhari, D.S. & Kakhandki, V.J. (2021). Comprehensive study and analysis of mechanical properties of chopped carbon fiber reinforced nylon 66 composite materials. Materials Today: Proceedings, 44 (6), 4596–4601.
- Du-Soon, Ch. & Yong-Taek, I. (1999). Prediction of shrinkage and warpage in consideration of residual stress in integrated simulation of injection molding. Tenth International Conference on Composite Structures, 47 (1-4), December, 655–665.
- Ghazali, M.F., Shayfull, Z., Azaman, M.D., Shuaib, N.A., & Manan, A. (2010). Introduction of Nylon-66 on Side Arm in a Catheter Manufacturing Process, International Journal of Engineering & Technology IJET/IJENS, 10 (6), 112–116.
- Goldberg, D.E. (2012). Genetic algorithms in search, optimization and machine learning. Mech. Sci. Technol., 26, 1133–1139.
- Herzog, B., Kohan, M.I., Mestemacher, S.A., Pagilagan, R.U., & Redmond, K. (2013). Polyamides. Ullmann’s Encyclopedia of Industrial Chemistry, VCH: Weinheim, Germany, 1–36.
- Houck, C.R., Jeffery, A.J., & Kay M.G. (1995). A Genetic Algorithm for Function Optimization: A Matlab Implementation. NCSU-1E TR95-09, 145–167.
- Jiang, W., Pang, Z., Wei, X., & Ping, X. (2007). Optimization of process parameters for thin plastic injection molding based on neural network and genetic algorithm, Modern Manufacturing Engineering, 1, 60–62.
- Li, W., Zhang, S., & Li, Y. (2002). Simulation and off-line optimization of an acrylonitrile fluidized-bed reactor based on artificial neural network. Chinese J. Chem. Eng., 10 (2), 198–201.
- Naveen, P., Chaitanya, M., Mayee, P.G., Bhanu Kiran G., Raghu, R., & Mohan, R. (2021). Design and optimization of nylon 66 reinforced composite gears using genetic algorithm. Journal of Materials Processing Technology, 514–519.
- Nuruzzaman, K.I., Asif Iqbal, R., Azhari, H., & Shin, Y. (2020). Influence of glass fiber content on tensile properties of polyamide-polypropylene based polymer blend composites. Journal of Materials Processing Technology, 133–137.
- Oktem, H., Erzurumlu, T., & Uzman, I. (2007). Application of Genetic Algorithm (GA) optimization technique in determining plastic injection molding process parameters for a thin-shell part. Mater. Des, 1271–1278.
- Sadeghi, B.H.M. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Materials Process Technology, 103, 411–416.
- Seow, L. (1997). Optimizing flow in plastic injection molding. Journal of Materials Processing Technology, 72, 333–341.
- Somjate, P. (2009). Defects in plastic injection molded parts Defect of Injection Molded Parts. Cause and Troubleshooting, 69, 113–134.
- Tsoukalas, V.D. (2009). Optimization of injection conditions for a thin walled die-cast part using a genetic algorithm method. Mold Flow Plastic Insight Release 3.0, 1097–1105.
- Yang, K. & EI-Haik, B.S. (2009). Design for Six Sigma: A Roadmap for Product Development, 2nd ed., McGraw-Hill Companies: New York, NY, USA.
- Zagar, E., Cesarek, U., Drinc, A., Sitar, S., Shlyapnikov, I., & Pahovnik, D. (2020). Quantitative Determination of PA6 and/or PA66 Content in PolyamideContaining Wastes, ACS Sustainable Chem. Eng., 11818–11826.
- Zhong, Y., He-Sheng, L., Tang-Qing, K., Xing-Yuan, H., Zhong-Shi, Ch., Wei, Z., & Kai, Z. (2020). The study of short-shot water-assisted injection molding of short glass fiber reinforced polypropylene. Journal of Applied Polymer Science, 1–12.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3df5ba66-f5bc-4818-82e8-35b5eee483a6
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