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Materials and Manufacturing Processes Volume 30, 2015 - Issue 4: Special Issue on Genetic Algorithms
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Original Articles

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

Yiğit M. ArisoyManufacturing and Automation Research Laboratory Department of Industrial and Systems Engineering, Rutgers University, Piscataway, New Jersey, USA
&
Tuğrul ÖzelManufacturing and Automation Research Laboratory Department of Industrial and Systems Engineering, Rutgers University, Piscataway, New Jersey, USACorrespondence[email protected]
Pages 425-433 | Received 27 May 2014, Accepted 10 Aug 2014, Published online: 13 Feb 2015

REFERENCES

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  • Moussaoui, K.; Mousseigne, M.; Senatore, J.; Chieragatti, R.; Monies, F. Influence of milling on surface integrity of Ti6Al4V—study of the metallurgical characteristics: Microstructure and microhardness. International Journal of Advanced Manufacturing Technology 2013, 67, 1477–1489.
  • Jovanovic, M.T.; Tadic, S.; Zec, S.; Miskovic, Z.; Bobic, I. The effect of annealing temperatures and cooling rates on microstructure and mechanical properties of investment cast Ti–6Al–4V alloy. Materials and Design 2006, 27, 192–199.
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  • Ding, R.; Guo, Z.X.; Wilson, A. Microstructural evolution of a Ti–6Al–4V alloy during thermomechanical processing. Material Science Engineering A-Structure 2002, 327 (2), 233–245.
  • Sima, M.; Özel, T. Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti-6Al-4V. International Journal of Machine Tools and Manufacture 2010, 50 (11), 943–960.
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  • Chakraborti, N.; Das, S.; Jayakanth, R.; Pekoz, R.; Erkoc, S. Genetic algorithms applied to Li+ions contained in carbon nanotubes: An investigation using particle swarm optimization and differential evolution along with molecular dynamics. Materials and Manufacturing Processes 2007, 22 (5–6), 562–569.
  • Chakraborti, N. Promise of multi-objective genetic algorithms in coating performance formulation. Surface Engineering 2014, 30 (2), 79–82.
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  • Chakraborti, N. Critical assessment: the unique contributions of multi-objective evolutionary and genetic algorithms in materials research. Material Science and Technology 2014, DOI:10.1179/1743284714Y.0000000578

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