Prediction of the mean grain size of MA-synthesized nanopowders by artificial neural networks
- 216 Downloads
In this work, mean grain size of synthesized nanomaterials (such as MoSi2–TiC and MoSi2–SiC), which were produced by mechanical alloying, has been modeled by artificial neural networks. A total number of 103 data were gathered from the previous works, trained, validated and tested by the built networks. The used data as inputs were the method of calculation of the mean grain size, milling time, annealing temperature, produced phases after mechanical alloying, vial speed and ball-to-powder ratio. The value of the output layer was the mean grain size. The obtained results from the testing phase of the trained networks showed that the models are capable of predicting the mean grain size of the mechanical-alloyed synthesized materials in the considered range.
KeywordsMechanical alloying Synthesized materials Nanopowders Artificial neural networks
- 17.Cullity BD (1977) Elements of X-ray diffraction, 2nd edn. Addison–Wesley, ReadingGoogle Scholar
- 24.Nazari A, Riahi S (2011) Artificial neural networks to prediction total specific pore volume of geopolymers produced from waste ashes. Neural Comput Appl. doi: 10.1007/s00521-011-0760-x
- 28.Suratgar AA, Tavakoli MB, Hoseinabadi A (2005) Modified Levenberg–Marquardt method for neural networks training. World Acad Sci Eng Technol 6:46–48Google Scholar