Optimización de parámetros en procesos de fundición a presión y compresión mediante el algoritmo de enjambre de partículas

Yordy González-Rondón; José Eduardo Rengel; Johnny J. Martínez

doi:10.36561/ING.21.5

Authors

Yordy González-Rondón Universidad de Oriente, Venezuela https://orcid.org/0000-0002-4769-7568
José Eduardo Rengel Universidad de Oriente, Venezuela https://orcid.org/0000-0002-4510-8846
Johnny J. Martínez Universidad de Oriente, Venezuela https://orcid.org/0000-0001-8901-9303

DOI:

https://doi.org/10.36561/ING.21.5

Keywords:

Parameter optimization, Squeeze casting, Die casting, PSO algorithm

Abstract

In this article, an algorithm, based on the particle swarm technique (PSO), is developed to optimize die casting and compression casting processes, using mathematical models to describe the behavior of both processes. In compression casting the mathematical model describes a problem with multiple objectives and constraints, and in die casting the model describes a single objective problem with constraints. The development of the PSO algorithm was carried out with the FORTRAN 90 software, and the results were compared with those reported by a teaching-learning based optimization algorithm, (TLBO), demonstrating good capabilities in the optimization of parameters in die casting and by compression. It was observed that the PSO algorithm achieves an optimal solution in all processes and the computational time were minimal.

Downloads

Download data is not yet available.

References

González-Rondón, Y. y Rengel-Hernández, J.E. (2021). Comportamiento termo fluidodinámico del acero en un molde de colada continua: una revisión, TecnoLógicas, vol. 24, nro. 51, e1856.

Venkata Rao, R. Kalyankar, V.D. and Waghmare G. (2014). Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm. Applied Mathematical Modelling, 38, 5592–5608.

Souissi, N. Souissi, S. Le Niniven, C. Ben Amar, M. Bradai, C. and Elhalouani, F. (2014). Optimization of Squeeze Casting Parameters for 2017 A Wrought Al Alloy Using Taguchi Method. Metals, 4, 141-154; doi:10.3390/met4020141.

Souissi, S. Souissi, N. Barhoumi, H. Ben Amar, M. Bradai, C. and Elhalouani, F. (2019). Characterization of the Role of Squeeze Casting on the Microstructure and Mechanical Properties of the T6 Heat Treated 2017A Aluminum Alloy. Hindawi, Advances in Materials Science and Engineering, Volume 2019, Article ID 4089537, 9 pages.

Belisario, J. (2001). Evaluación de la efectividad del proceso de colada en la reducción de defectos de salpicaduras en las palanquillas producidas en la acería 150 TM de Sidor, Tesis de grado en ingeniería industrial. Universidad Nacional Experimental de Guayana

Maleki, A. Shafyei, A. and Niroumand, B. (2009). Effects of squeeze casting parameters on the microstructure of LM13 alloy. Journal of Materials Processing Technology, vol. 209, no. 8, pp. 3790–3797.

Dahel, A. Nogita, K. Mcdonald, S. Zindel, J. and Hogan, L. (2001). Eutectic nucleation and growth in hypoeutectic Al-Si alloys at different strontium levels. Metallurgical and Materials Transactions A, vol. 32, no. 4, pp. 949–960.

Fang, Y. (2006). Mathematical modeling and experimental study of squeeze casting of magnesium alloy AM50A and aluminum alloy A356. Electronic Theses and Dissertations. 7212.

Guo, H. y Yang, X. (2007). Preparation of semi-solid slurry containing fine and globular particles for wrought aluminium alloy 2024. Trans. Nonferr. Metal Soc. 2007, 17, 799–804.

Han, Y.S. Kim, D.H. Lee, H.I. and Kim, Y.G. (1994). Effect of applied pressure during solidification on the microstructural refinement in an Al-Cu alloy. Scripta Metallurgica et Materialia, Vol.31, No.12, pp.1623-1628.

Maeng, D.Y., Lee, J.H., Won, C.W., Cho, S.S. and Chun, B.S. (2000). The effects of processing parameters on the microstructure and mechanical properties of modified B390 alloy in direct squeeze casting. J. of Materials Processing Technology, Vol.105, pp.196-203.

Hong, C.P. Shen, H.F. and Lee, S.M. (1998). Prevention of macrodefects in squeeze casting of an Al-7 wt pct Si alloy. Metallurgical and Materials Transactions B, Vol.31B, pp.297-305.

Hong, C.P. Shen, H.F. and Cho, I.S. (1998). Prevention of macrosegregation in squeeze casting of an Al-4.5 wt pct Cu alloy. Metallurgical and Materials Transactions A, Vol.29A, pp.339-349.

Gallerneault, M. Durrant, G. and Cantor, B. (1996). The squeeze casting of hypoeutectic binary Al-Cu. Metallurgical and Materials Transactions A, Vol.27A, pp.4121-4132.

Mahesh N. A. and Shrikant V. K. (2015). Optimization of die-casting process parameters using DOE. International Journal of Engineering Research and General Science Volume 3, Issue 2.

Kumar, S. Kumar Gupta, A. and Chandna, P. (2012). Optimization of Process Parameters of Pressure Die Casting using Taguchi Methodology. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol:6, No:8.

Ainul Haque, M. D. Babuli Kumar, J. and Dilip Kumar, M. (2017). Optimization of process parameters in cold chamber Pressure die casting using doe. International Research Journal of Engineering and Technology (IRJET). Volume: 04 Issue: 04, Apr.

Vanli, A.S. Akdogan, A.and Durakbasa, M.N. (2018). Process Optimization and Surface Modification of Die Casting AZ91 Magnesium Alloy Products. Acta physica polonica A. Vol. 134.

Gallardo, J. Ayala, C. y Castro, R. (2018). Control predictivo/adaptativo de sistemas complejos utilizando técnicas de ingeniería neuronal. Revista Ingenierías Universidad de Medellín, Vol. 17 Núm. 33, julio-diciembre, pp. 157-172, Medellín, Colombia.

García, J. (2006). Algoritmos Basados en Cúmulos de Partículas para la Resolución de Problemas Complejos. Departamento de Lenguajes y Ciencias de la computación. Universidad de Málaga.

Kennedy, J. and Eberhart, R. (1995). Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks, volume 4, pages 1942-1948, Perth, Australia, Nov.

Kennedy, J. and Eberhart, R. and Shi, Y. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann Publishers.

Stephan, O. and Zomaya, A. (2005). Handbook Of Bioinspired Algorithms And Applications. CHAPMAN and HALL/CRC.

Bäck, T. Fogel, D. and Michalewicz, Z. (1997). Handbook of Evolutionary Computation. IOP Publishing and Oxford University Press, New York and Bristol (UK), Feb.

Dorigo, M. (2000). The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances. Technical Report IRIDIA-2000-32, Universit'e Libre de Bruxelles, IRIDIA.

Rao, R. Savsani, V. and Vakharia, D. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems, Comput. Aided Des. 43, 303–315.

Rao, R. Savsani, V. Vakharia, D. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems, Inf. Sci. 183, 1–15.

Rao, R. and Patel, V. (2012). An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems, Int. J. Ind. Eng. Comput. 3 (4), 535–560.

Patel, G.C.M. Krishna, P. Vundavilli, P.R. Parappagoudar, M.B. (2016). Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization. Archives of Foundry Engineering 16(3).

Shu Fu-hua. (2007). Aluminum-zinc Alloy Squeeze Casting Technological Parameters Optimization based on PSO and ANN. China Foundry, vol.4, No.3, pp.202-205.

Panicker, P. and Kuriakose, S. (2020) Parameter Optimisation of Squeeze Casting Process using LM 20 Alloy: Numeral Analysis by Neural Network and Modified Coefficient-based Deer Hunting Optimization. Australian Journal of Mechanical Engineering

Wu, H. Yang, X. Cao, G. Zhao, L. and Yang, L. (2021). Design and optimisation of die casting process for heavy-duty automatic transmission oil circuit board. International Journal of Cast Metals Research (IF 1.083).

Patel G.C.M., Ajith B, Jonathan R, Allan D S, Aniruddh M and Ashwith M. (2018). Teaching learning based optimization of squeeze casting process for quality castings. IOP Conf. Ser.: Mater. Sci. Eng. 376 012112

Senthil, P. and Amirthagadeswaran, K.S. (2012). Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method, J. Mech. Sci. Technol. 26 (4), 1141–1147.

Tsoukalas, V. (2008). Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis, Mater. Des. 29, 2027–2033.

Talbi, E. G. (2009). Metaheuristics: From Design to Implementation. New Jersey, Hoboken: John Wiley & Sons, Inc.

Chun, H. L. (2012). Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO. Tesis para optar al grado de Magister en ciencias mención computación. Universidad de Chile Facultad de física y matemáticas, Departamento de ciencias de la computación.

Leza, M. V. (2008). Optimización mediante Cúmulos de Partículas con Tamaño de Población Variable. Tesis de Licenciatura en Sistemas Facultad de Informática. Universidad Nacional de la Plata.

Gutiérrez, D. Villa, W. y López-Lezama, J. (2017). Flujo Óptimo Reactivo mediante Optimización por Enjambre de Partículas. Información Tecnológica. Vol. 28(5), 215-224.

Damiani, L. (2019). Optimización estocástica acelerada con aplicación a la ingeniería de procesos. Tesis de maestría en ingeniería de procesos petroquímicos. Universidad Nacional del Sur.