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Optimization of the EMS process parameters in compocasting of high-wear-resistant Al-nano-TiC composites

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Abstract

Understanding of the electromagnetic stirrer (EMS) process parameters–wear relation in nanocomposite is required for further creation of tailored modifications of process in accordance with the demands for various applications. This study depicts the performance of hybrid algorithm for optimization of the parameters in EMS compocasting of nano-TiC-reinforced Al–Si alloys. Adaptive neuro-fuzzy inference system (ANFIS) coupled with particle swarm optimization (PSO) was applied to find the optimum combination of the inputs including mold temperature, mix time, impeller speed, powder temperature, cast temperature and average particle size. The optimized condition was obtained in minimization of objective function. The objective function is calculated by ANFIS and then minimized by PSO. The optimized parameters were used to produce semisolid cast aluminum matrix composites reinforced with nano-TiC particles. The optimized nanocomposites were then studied for their tribological properties.

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References

  1. Z. Shi, J.M. Yang et al., The melt structural characteristics concerning the interfacial reaction in SiC(p)/Al composites. Appl. Phys. A 71, 203–209 (2000)

    Article  ADS  Google Scholar 

  2. A. Baghani, A. Bahmani et al., Numerical investigation of the effect of sprue base design on the flow pattern of aluminum gravity casting. Defect. Diffus. Forum. 344, 43–53 (2013)

    Article  Google Scholar 

  3. M.O. Shabani, A. Mazahery, Prediction of mechanical properties of cast A356 alloy as a function of microstructure and cooling rate. Arch. Metall. Mater. 56, 671–675 (2011)

    Google Scholar 

  4. Z. Shi, S. Ochiai et al., The formation and thermostability of MgO and MgAl2O4 nanoparticles in oxidized SiC particle-reinforced Al–Mg composites. Appl. Phys. A 74, 97–104 (2002)

    Article  ADS  Google Scholar 

  5. A. Mazahery, M.O. Shabani, Mechanical properties of A356 matrix composites reinforced with nano-SiC particles. Strength Mater. (2012). doi:10.1007/s11223-012-9423-01-7

    Google Scholar 

  6. M. Razavi, A.H. Rajabi-Zamani et al., Synthesis of Fe–TiC–Al2O3 hybrid nanocomposite via carbothermal reduction enhanced by mechanical activation. Ceram. Int. 37, 443–449 (2011)

    Article  Google Scholar 

  7. A. Mazahery, M.O. Shabani, Microstructural and abrasive wear properties of SiC reinforced aluminum-based composite produced by compocasting. Trans. Nonferr. Met. Soc. China (English Edition) 23, 1905–1914 (2013)

    Article  Google Scholar 

  8. M.R. Rahimipour, A.A. Tofigh et al., The enhancement of wear properties of compo-cast A356 composites reinforced with Al2O3 nano particulates. Tribol. Ind. 36, 220–227 (2014)

    Google Scholar 

  9. M.O. Shabani, A. Mazahery, The synthesis of the particulates Al matrix composites by the compocasting method. Ceram. Int. 39, 1351–1358 (2013)

    Article  Google Scholar 

  10. A. Mazahery, M.O. Shabani, Tribological behaviour of semisolid-semisolid compocast Al–Si matrix composites reinforced with TiB 2 coated B 4C particulates. Ceram. Int. 38, 1887–1895 (2012)

    Article  Google Scholar 

  11. M.O. Shabani, A. Mazahery, Suppression of segregation, settling and agglomeration in mechanically processed composites fabricated by a semisolid agitation processes. Trans. Indian Inst. Met. 66, 65–70 (2013)

    Article  Google Scholar 

  12. C. Vivès, Crystallization of semi-solid magnesium alloys and composites in the presence of magnetohydrodynamic shear flows. J. Cryst. Growth 137, 653–662 (1994)

    Article  ADS  Google Scholar 

  13. A.A. Tofigh, M.R. Rahimipour et al., Application of the combined neuro-computing, fuzzy logic and swarm intelligence for optimization of compocast nanocomposites. J. Compos. Mater. 49, 1653–1663 (2015)

    Article  ADS  Google Scholar 

  14. A. Mazahery, M.O. Shabani, Assistance of novel artificial intelligence in optimization of aluminum matrix nanocomposite by genetic algorithm. Metall. Mater. Trans. A 43, 5279–5285 (2012)

    Article  Google Scholar 

  15. M.O. Shabani, M. Alizadeh et al., Modelling of mechanical properties of cast A356 alloy. Fatigue Fract. Eng. Mater. Struct. 34, 1035–1040 (2011)

    Article  Google Scholar 

  16. A. Baghani, A. Bahmani et al., Application of computational fluid dynamics to study the effects of Sprue base geometry on the surface and internal turbulence in gravity casting. Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl. 229, 106–116 (2015)

    Google Scholar 

  17. A. Mazahery, M.O. Shabani, The accuracy of various training algorithms in tribological behavior modeling of A356-B 4C composites. Russ. Metall. (Metally) 2011, 699–707 (2011)

    Article  ADS  Google Scholar 

  18. J.L. de la Peña, M.I. Pech-Canul, Wetting behavior of Al–Si–Mg alloys on Si3N4/Si substrates: optimization of processing parameters. Appl. Phys. A 91, 545–550 (2008)

    Article  ADS  Google Scholar 

  19. A.H. Faraji, A. Bahmani et al., Numerical and experimental investigations of weld pool geometry in GTA welding of pure aluminum. J. Cent. South Univ. 21, 20–26 (2014)

    Article  Google Scholar 

  20. A. Mazahery, M.O. Shabani, Modification mechanism and microstructural characteristics of eutectic si in casting Al–Si Alloys: a review on experimental and numerical studies. JOM 66, 726–738 (2014)

    Article  ADS  Google Scholar 

  21. M.O. Shabani, A. Mazahery, Automotive copper and magnesium containing cast aluminium alloys: report on the correlation between Yttrium modified microstructure and mechanical properties. Russ. J. Non-Ferr. Met. 55, 436–442 (2014)

    Article  Google Scholar 

  22. A. Baghani, P. Davami et al., Investigation on the effect of mold constraints and cooling rate on residual stress during the sand-casting process of 1086 steel by employing a thermomechanical model. Metall. Mater. Trans. B 45, 1157–1169 (2014)

    Article  Google Scholar 

  23. A. Bahmani, G.B. Eisaabadi et al., Effects of hydrogen level and cooling rate on ultimate tensile strength of Al A319 alloy. Russ. J. Non-Ferr. Met. 55, 365–370 (2014)

    Article  Google Scholar 

  24. M.O. Shabani, A. Mazahery et al., Solidification of A356 Al alloy: experimental study and modeling. Kov. Mater. 49, 253–258 (2011)

    Google Scholar 

  25. A. Bahmani, N. Hatami et al., A mathematical model for prediction of microporosity in aluminum alloy A356. Int. J. Adv. Manuf. Technol. 64, 1313–1321 (2013)

    Article  Google Scholar 

  26. M.O. Shabani, A. Mazahery et al., Silicon morphology modelling during solidification process of A356 Al alloy. Int. J. Cast Met. Res. 25, 53–58 (2012)

    Article  Google Scholar 

  27. A. Mazahery, M.O. Shabani, Investigating the effect of reinforcing particulates on the weight loss and worn surface of compocast AMCs. Kov. Mater. 51, 11–18 (2013)

    Google Scholar 

  28. M.R. Rahimipour, A.A. Tofigh et al., Strategic developments to improve the optimization performance with efficient optimum solution and produce high wear resistance aluminum-copper alloy matrix composites. Neural Comput. Appl. 24, 1531–1538 (2014)

    Article  Google Scholar 

  29. M.R. Rahimipour, A.A. Tofigh et al., Enhancement of abrasive wear resistance in consolidated Al matrix composites via extrusion process. Tribol. Mater. Surf. Interfaces 7, 129–134 (2013)

    Article  Google Scholar 

  30. A. Mazahery, M.O. Shabani, A comparative study on abrasive wear behavior of semisolid-liquid processed Al–Si matrix reinforced with coated B 4C reinforcement. Trans. Indian Inst. Met. 65, 145–154 (2012)

    Article  Google Scholar 

  31. M.O. Shabani, A. Mazahery, Prediction performance of various numerical model training algorithms in solidification process of A 356 matrix composites. Indian. J. Eng. Mater. Sci. 19(2), 129–134 (2012)

    Google Scholar 

  32. C. Vives, J. Bas et al., Fabrication of metal matrix composites using a helical induction stirrer. Mater. Sci. Eng. A 173, 239–242 (1993)

    Article  Google Scholar 

  33. A. Mazahery, M. Alizadeh et al., Study of tribological and mechanical properties of A356-nano SiC composites. Trans. Indian Inst. Met. 65, 393–398 (2012)

    Article  Google Scholar 

  34. M.O. Shabani, M.R. Rahimipour et al., Refined microstructure of compo cast nanocomposites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques. Neural Comput. Appl. 26, 899–909 (2015)

    Article  Google Scholar 

  35. Y.-K. Lam, P.W.M. Tsang et al., PSO-based K-Means clustering with enhanced cluster matching for gene expression data. Neural Comput. Appl. 22, 1349–1355 (2013)

    Article  Google Scholar 

  36. A. Mazahery, M.O. Shabani, Elaboration of an operative and efficacious optimization route to ameliorate the mechanical and tribological properties of implants. Powder Technol. 249, 530–535 (2013)

    Article  Google Scholar 

  37. A.K. Pani, H.K. Mohanta, Soft sensing of particle size in a grinding process: application of support vector regression, fuzzy inference and adaptive neuro fuzzy inference techniques for online monitoring of cement fineness. Powder Technol. 264, 484–497 (2014)

    Article  Google Scholar 

  38. T. Rajabloo, A. Ghafarinazari et al., Taguchi based fuzzy logic optimization of multiple quality characteristics of cobalt disulfide nanostructures. J. Alloys Compd. 607, 61–66 (2014)

    Article  Google Scholar 

  39. M.O. Shabani, A. Mazahery, Aluminum-matrix nanocomposites: swarm-intelligence optimization of the microstructure and mechanical properties. Mater. Tehnol. 46, 613–619 (2012)

    Google Scholar 

  40. M. Sheikhan, N. Mohammadi, Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural Comput. Appl. 23, 1185–1194 (2013)

    Article  Google Scholar 

  41. A.A. Tofigh, M.R. Rahimipour et al., Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites. J. Manuf. Process. 15, 518–523 (2013)

    Article  Google Scholar 

  42. İ. Karahan, R. Ozdemir et al., A comparison of genetic programming and neural networks; new formulations for electrical resistivity of Zn–Fe alloys. Appl. Phys. A 113, 459–476 (2013)

    Article  ADS  Google Scholar 

  43. M.O. Shabani, A. Mazahery, Application of a linearly decreasing weight particle swarm to optimize the process conditions of al matrix nanocomposites. Metallurgist (2012). doi:10.1007/s11015-012-9591-y1-9

    Google Scholar 

  44. V. Vijayaraghavan, A. Garg et al., Estimation of mechanical properties of nanomaterials using artificial intelligence methods. Appl. Phys. A 116, 1099–1107 (2014)

    Article  ADS  Google Scholar 

  45. M.O. Shabani, A. Mazahery et al., The most accurate ANN learning algorithm for FEM prediction of mechanical performance of alloy A356. Kov. Mater. 50, 25–31 (2012)

    Google Scholar 

  46. A.A. Tofigh, M.O. Shabani, Efficient optimum solution for high strength Al alloys matrix composites. Ceram. Int. 39, 7483–7490 (2013)

    Article  Google Scholar 

  47. A. Ramil, A.J. López et al., Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser induced breakdown spectroscopy (LIBS). Appl. Phys. A 92, 197–202 (2008)

    Article  ADS  Google Scholar 

  48. S. Shamshirband, A. Malvandi et al., Performance investigation of micro- and nano-sized particle erosion in a 90° elbow using an ANFIS model. Powder Technol. 284, 336–343 (2015)

    Article  Google Scholar 

  49. A. Mazahery, M.O. Shabani, Development of the principle of simulated natural evolution in searching for a more superior solution: proper selection of processing parameters in AMCs. Powder Technol. 245, 146–155 (2013)

    Article  Google Scholar 

  50. M.O. Shabani, A. Mazahery, Optimization of Al matrix reinforced with B4C particles. JOM 65, 272–277 (2013)

    Article  Google Scholar 

  51. U. Aich, S. Banerjee, Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl. Math. Model. 38, 2800–2818 (2014)

    Article  Google Scholar 

  52. A. Mazahery, M.O. Shabani et al., Concurrent fitness evaluations in searching for the optimal process conditions of Al matrix nanocomposites by linearly decreasing weight. J. Compos. Mater. 47, 1765–1772 (2013)

    Article  ADS  Google Scholar 

  53. M.O. Shabani, A. Mazahery, Computational modeling of cast aluminum 2024 alloy matrix composites: adapting the classical algorithms for optimal results in finding multiple optima. Powder Technol. 249, 77–81 (2013)

    Article  Google Scholar 

  54. M. Chih, C.-J. Lin et al., Particle swarm optimization with time-varying acceleration coefficients for the multidimensional knapsack problem. Appl. Math. Model. 38, 1338–1350 (2014)

    Article  MathSciNet  Google Scholar 

  55. C. Sudheer, R. Maheswaran et al., A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput. Appl. 24, 1381–1389 (2014)

    Article  Google Scholar 

  56. A. Mazahery, M.O. Shabani et al., Searching for the superior solution to the population-based optimization problem: processing of the wear resistant commercial AA6061 AMCs. Int. J. Damage Mech. 23, 899–916 (2014)

    Article  Google Scholar 

  57. M.O. Shabani, A. Mazahery, Searching for a novel optimization strategy in tensile and fatigue properties of alumina particulates reinforced aluminum matrix composite. Eng. Comput. 30, 559–568 (2012)

    Article  Google Scholar 

  58. F. Heydari, A. Maghsoudipour et al., Modeling of thermal expansion coefficient of perovskite oxide for solid oxide fuel cell cathode. Appl. Phys. A 120, 1625–1633 (2015)

    Article  ADS  Google Scholar 

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Acknowledgments

This Research work was supported by Najafabad branch, Islamic Azad University under grant of research project “Optimization of mechanical properties and microstructure of nano composite Al-TiC in casting process”.

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Correspondence to Majid Shamsipour.

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Shamsipour, M., Pahlevani, Z., Shabani, M.O. et al. Optimization of the EMS process parameters in compocasting of high-wear-resistant Al-nano-TiC composites. Appl. Phys. A 122, 457 (2016). https://doi.org/10.1007/s00339-016-9840-1

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