Artificial neural network (ANN) approach to predicting micro hardness profile values of iron-based sintered alloys

Authors

  • Desislava Yordanova Mincheva Technical university of Varna
  • Georgi Stefanov Antonov Technical University of Varna

DOI:

https://doi.org/10.29114/ajtuv.vol1.iss1.23

Keywords:

PM sintered compacts, sintering atmosphere, heating rate, micro hardness, artificial neural network

Abstract

Recent interest in artificial neural networks has considerably extended their use in the field of powder metallurgy. Advanced in the paper is a model for predicting the micro hardness of sintered compacts made from iron powders and powder mixtures through the process of sintering performed in different atmospheres. The proposed model is based on three layer neural network with backpropagation learning algorithm. Specially developed software has been used to provide for the proper functioning of the neural network. Moreover, it should also be noted that the training data used to carry out the research has been collected by a laboratory controlled experimental testing. Finally, the paper concludes that the presented neural network model is applicable for hardness profile prediction of iron-based sintered alloys as confirmed by the experimental results.

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References

<p>Callister. (2006). Materials sciences and engineering, An introduction 7th edition. <br /><br />Cherian, R. P., Smith, L. N., &amp; Midha, P. S. (2000). A neural network approach for selection of powder metallurgy materials and process parameters. Artificial Intelligence in Engineering, 14(1), 39-44. <br /><a href="https://doi.org/10.1016/S0954-1810(99)00026-6">Crossref</a><br /><br /> Dimitrov D, Zlateva P, Pieszonka T, &amp; Stoychev M. (2011). Atmosphere effect on dimensional changes during sintering of SC100.26 Iron powder with graphite and copper additions. Journal of materials science and technology, 19(4), 245-253. <br /><br />Drandarevic, D. (2000). Accuracy modeling of powder metallurgy process using backpropagation neural networks. Powder metallurgy, 25-29.<br /> <a href="https://doi.org/10.1179/pom.2000.43.1.25">Crossref</a> <br /><br />Drnadaveric. (2005). Modeling of dimensional changes during sintering. Science of sintering, 181-187. <br /><br />Fausett. (1994). Fundamentals of neural network: architectures, algorithms and applications. Prentice-Hall. <br /><br />Leonov, &amp; Nikolov V. (2012). A wavelet and neural network model for prediction of dry bulk shipping indices. Maritime economics and logistics, 319-333.<br /> <a href="https://doi.org/10.1057/mel.2012.10">Crossref</a> <br /><br />Mincheva D. (2016). Influence of the sintering atmosphere on the structure and properties of iron sintered alloys with addition of 2%Ti. ECOVARNA, 2016, (pp. 95-101). Varna.<br /><br /> Ohdar, R. (2003, January). Prediction or the process parameters of metal powders perform forging using artificial neural network (ANN). Journal of materials processing technology. <br /><br />Stoychev M., Rusev R., &amp; Harizanova S. (2002). Microstructures of sintered and heat-treated steels alloyed with Mn, Cr and Mo. International PM Conference. Ankara, Turkey. <br /><br />Sudhacar K, M. E. (2001, February). Mechanical behavior of powder metallurgy steel-Experimental Investigation and Artificial neural network-based prediction model. Journal of Materials Engineering and Performance, Vol. 10, Issue 1, pp. 31-36.<br /><a href="https://doi.org/10.1361/105994901770345303" target="_blank" rel="noopener">https://doi.org/10.1361/105994901770345303</a></p>

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Published

2017-12-28

How to Cite

Mincheva, D. Y., & Antonov, G. S. (2017). Artificial neural network (ANN) approach to predicting micro hardness profile values of iron-based sintered alloys. ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA, 1(1), 1–5. https://doi.org/10.29114/ajtuv.vol1.iss1.23

Issue

Vol. 1 No. 1 (2017): Annual Journal of Technical university of Varna

Section

MECHANICS, MATERIALS AND MECHANICAL ENGINEERING

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