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Bayesian Regularization Neural Networks for Prediction of Austenite Formation Temperatures (Ac1 and Ac3)

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Abstract

A neural network with a feed forward topology and Bayesian regularization training algorithm is used to predict the austenite formation temperatures (Ac1 and Ac3) by considering the percentage of aloying elements in chemical composition of steel. The data base used here involves a large variety of diferent steel types such as structural steels, stainless steels, rail steels, spring steels, high temperature creep resisting steels and tool steels. Scater diagrams and mean relative error (MRE) statistical criteria are used to compare the performance of developed neural network with the results of Andrew s empirical equations and a feed forward neural network with “gradient descent with momentum’ training algorithm. The results showed that Bayesian regularization neural network has the best performance. Also, due to the satisfactory results of the developed neural network, it was used to investigate the effect of the chemical composition on Ac1 and Ac3 temperatures. Results are in accordance with materials science theories.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Birjand University of Technology, Birjand, South Khorasan, 97175-569, Iran

    Masoud Rakhshkhorshid (Doctor, Assistant Professor)

  2. Department of Mechanical Engineering, Roudbar Branch, Islamic Azad University, Roudbar, Guilan, 44615-1146, Iran

    Sayyed-Amin Teimouri Sendesi (Master, Lectureshi)

Authors
  1. Masoud Rakhshkhorshid
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  2. Sayyed-Amin Teimouri Sendesi
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Correspondence to Sayyed-Amin Teimouri Sendesi.

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Rakhshkhorshid, M., Teimouri Sendesi, SA. Bayesian Regularization Neural Networks for Prediction of Austenite Formation Temperatures (Ac1 and Ac3). J. Iron Steel Res. Int. 21, 246–251 (2014). https://doi.org/10.1016/S1006-706X(14)60038-8

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  • DOI: https://doi.org/10.1016/S1006-706X(14)60038-8

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