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Phase Identification of La-Doped Hard Magnetic Barium Ferrite Using Artificial Neural Network

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Abstract

A model based on an artificial neural network (ANN) was designed for the simulation and estimation of 2 theta and intensity values obtained by X-Ray Diffraction (XRD) of pure and La-doped barium ferrite powders which have been synthesized in ammonium nitrate melt. Its performance is evaluated by the influences of different La content, sintering temperature, Fe/Ba ratio, and washed in HCl (or not washed in HCl) samples. The XRD patterns of samples estimated by the ANN agree well with the experimental values, indicating that the model is reliable and adequate.

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Authors and Affiliations

  1. TUBITAK-UME, National Metrology Institute, PO Box 54, 41470, Gebze-Kocaeli, Turkey

    Huseyin Sozeri

  2. Physics Department, Faculty of Arts and Sciences, Uludag University, Gorukle Campus, 16059, Bursa, Turkey

    Ilker Kucuk

  3. Department of Physics, Middle East Technical University, 06531, Ankara, Turkey

    Husnu Ozkan

Authors
  1. Huseyin Sozeri
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  2. Ilker Kucuk
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  3. Husnu Ozkan
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Correspondence to Ilker Kucuk.

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Sozeri, H., Kucuk, I. & Ozkan, H. Phase Identification of La-Doped Hard Magnetic Barium Ferrite Using Artificial Neural Network. J Supercond Nov Magn 24, 683–687 (2011). https://doi.org/10.1007/s10948-010-0937-z

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  • DOI: https://doi.org/10.1007/s10948-010-0937-z

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