Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T15:31:45.179Z Has data issue: false hasContentIssue false

A new predictive model for calculating the hardness of metal matrix nanocomposites produced by mechanical alloying

Published online by Cambridge University Press:  19 November 2013

Majid Abdellahi*
Affiliation:
Materials Engineering Department, Islamic Azad University, Najafabad Branch, Najafabad, Iran
*
a)Address all correspondence to this author. e-mail: [email protected]
Get access

Abstract

In the present work, it has been suggested that the gene expression programing is a good tool for determination of hardness of metal matrix nanocomposite produced by mechanical alloying (MA). For example, we studied the Al matrix nanocomposite, and to build the models, 35 input-target data were gathered from the literature, randomly divided into 28 and 7 data sets and then were respectively trained and tested by the proposed models. The differences between the models were in their gene number, chromosomes, and head size. The amount of reinforcement, ball to powder ratio, compaction pressure, milling time, reinforcement hardness, sintering temperature, sintering time, and vial speed were 8 independent input parameters. The output parameter was mean hardness of nanocomposites. The results indicate that gene expression programing is a powerful tool for predicting the hardness of the nanocomposite produced by MA.

Type
Articles
Copyright
Copyright © Materials Research Society 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Scamans, G.M., Birbilis, N., and Buchheit, R.G.: Corrosion of aluminum and its alloys. In Shreir's Corrosion, Vol. 3, Elsevier, 2010; p. 1974.CrossRefGoogle Scholar
Shorowordi, K.M., Laoui, T., Haseeb, A., Celis, J.P., and Froyen, L.: Microstructure and interface characteristics of B4C, SiC and Al2O3 reinforced Al matrix composites: A comparative study. J. Mater. Process. Technol. 142, 738 (2003).CrossRefGoogle Scholar
Kumar, S., Panwar, R.S., and Pandey, O.P.: Effect of dual reinforced ceramic particles on high temperature tribological properties of aluminum composites. Ceram. Int. 39, 6333 (2013).CrossRefGoogle Scholar
Wang, Z., Song, M., Sun, C., and He, Y.: Effects of particle size and distribution on the mechanical properties of SiC reinforced Al–Cu alloy composites. Mater. Sci. Eng., A 528, 1131 (2011).CrossRefGoogle Scholar
Trapp, J. and Kieback, B.: Solid-state reactions during high-energy milling of mixed powders. Acta Mater. 61, 310 (2013).CrossRefGoogle Scholar
Yadav, T.P., Yadav, R.M., and Singh, D.P.: Mechanical milling: A top down approach for the synthesis of nanomaterials and nanocomposites. Nanosci. Nanotechnol. 2, 22 (2012).CrossRefGoogle Scholar
Kubota, M., Kaneko, J., and Kaneko, M.: Properties of mechanically milled and spark plasma sintered Al–AlB2 and Al–MgB2 nano-composite materials. Mater. Sci. Eng., A 475, 96 (2008).CrossRefGoogle Scholar
Arik, H.: Production and characterization of in situ Al4C3 reinforced aluminum-based composite produced by mechanical alloying technique. Mater. Des. 25, 31 (2004).CrossRefGoogle Scholar
Maiti, R. and Chakraborty, M.: Synthesis and characterization of molybdenum aluminide nanoparticles reinforced aluminum matrix composites. J. Alloys Compd. 458, 450 (2008).CrossRefGoogle Scholar
Abdoli, H., Salahi, E., Farnoush, H., and Pourazrang, K.: Evolutions during synthesis of Al–AlN-nanostructured composite powder by mechanical alloying. J. Alloys Compd. 461, 166 (2008).CrossRefGoogle Scholar
Abdoli, H., Asgharzadeh, H., and Salahi, E.: Sintering behavior of Al–AlN-nanostructured composite powder synthesized by high-energy ball milling. J. Alloys Compd. 473, 116 (2009).CrossRefGoogle Scholar
Naya, S.S., Pabi, S.K., and Murty, B.S.: Al–(L12)Al3 Ti nanocomposites prepared by mechanical alloying: Synthesis and mechanical properties. J. Alloys Compd. 492, 128 (2010).Google Scholar
Bustamante, R.P., Guel, I.E., Flores, W.A., Yoshida, M.M., Ferreira, P.J., and Sanchez, R.M.: Novel Al-matrix nanocomposites reinforced with multi-walled carbon nanotubes. J. Alloys Compd. 450, 323 (2008).CrossRefGoogle Scholar
Kubota, M. and Cizek, P.: Synthesis of Al3BC from mechanically milled and spark plasma sintered Al–MgB2 composite materials. J. Alloys Compd. 457, 209 (2008).CrossRefGoogle Scholar
El-Eskandarany, M.S.: Mechanical solid state mixing for synthesizing of SiC/Al nanocomposites. J. Alloys Compd. 279, 263 (1998).CrossRefGoogle Scholar
Sadeghian, Z., Lotfi, B., Enayati, M.H., and Beiss, P.: Microstructural and mechanical evaluation of Al–TiB2 nanostructured composite fabricated by mechanical alloying. J. Alloys Compd. 509, 7758 (2011).CrossRefGoogle Scholar
Paschke, H., Weber, M., Kaestner, P., and Braeuer, G.: Influence of different plasma nitriding treatments on the wear and crack behavior of forging tools evaluated by rockwell indentation and scratch tests. Surf. Coat. Technol. 205, 1465 (2010).CrossRefGoogle Scholar
Wang, M., Wang, D., Hopfeld, M., Grieseler, R., Rossberg, D., and Schaaf, P.: Nanoindentation of nano-Al/Si3N4 multilayers with Vickers and Brinell indenters. J. Eur. Ceram. Soc. 33(12), 23552358 (2013).CrossRefGoogle Scholar
Zisis, T.H.: Analysis of knoop indentation of cohesive frictional materials. Mech. Mater. 57, 53 (2013).Google Scholar
Bassi, A.C., Casa, F., and Mendichi, R.: Shore a hardness and thickness. Polym. Test. 7, 165 (1987).CrossRefGoogle Scholar
Baykasoğlu, A., Güllü, H., Çanakçı, H., and Özbakır, L.: Prediction of compressive and tensile strength of limestone via genetic programing. Expert Syst. Appl. 35, 111 (2008).CrossRefGoogle Scholar
Yeh, I.C. and Lien, L.C.: Knowledge discovery of concrete material using genetic operation trees. Expert Syst. Appl. 36, 5807 (2009).CrossRefGoogle Scholar
Wahab Khan, M. and Alam, M.: A survey of application: Genomics and genetic programming, a new frontier. Genomics 100, 65 (2012).CrossRefGoogle Scholar
Sarıdemir, M.: Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Constr. Build. Mater. 24, 1911 (2010).CrossRefGoogle Scholar
Fatih Kara, I.: Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming. Adv. Eng. Soft. 42, 295 (2011).CrossRefGoogle Scholar
Worzel, W.P., Yu, J., Almal, A.A., and Chinnaiyan, M.: Applications of genetic programming in cancer research. Int. J. Biochem. Cell Biol. 41, 405 (2009).CrossRefGoogle ScholarPubMed
Sarıdemir, M.: Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming. Expert Syst. Appl. 38, 14257 (2011).Google Scholar