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Materials and Manufacturing Processes Volume 24, 2008 - Issue 1
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Original Articles

Exploring the Possibilities of Development of Directly Quenched TRIP-Aided Steel by the Artificial Neural Networks (ANN) Technique

K. P. Das School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, India
,
S. Ganguly School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, India
,
P. P. Chattopadhyay Department of Metallurgy and Materials Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India
,
S. Tarafder National Metallurgical Laboratory, Jamshedpur, India
&
N. R. Bandyopadhyay School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, IndiaCorrespondence[email protected]
Pages 68-77 | Received 23 Feb 2008, Accepted 25 Aug 2008, Published online: 02 Mar 2009

Citations (5)

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S Datta & P P Chattopadhyay. (2013) Soft computing techniques in advancement of structural metals. International Materials Reviews 58:8, pages 475-504.
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Articles from other publishers (4)

Tarun Nanda, Vishal Singh, Virender Singh, Arnab Chakraborty & Sandeep Sharma. (2016) Third generation of advanced high-strength steels: Processing routes and properties. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 233:2, pages 209-238.
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S. Ganguly, A. Patra, P.P. Chattopadhyay & S. Datta. (2016) New training strategies for neural networks with application to quaternary Al–Mg–Sc–Cr alloy design problems. Applied Soft Computing 46, pages 260-266.
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Tanmay Bhattacharyya, Shiv Brat Singh, Swati Sikdar (Dey)Sandip Bhattacharyya, Wolfgang Bleck & Debashish Bhattacharjee. (2013) Microstructural prediction through artificial neural network (ANN) for development of transformation induced plasticity (TRIP) aided steel. Materials Science and Engineering: A 565, pages 148-157.
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Bekir Sadık Ünlü, Hülya Durmuş & Cevdet Meriç. (2012) Determination of tribological properties at CuSn10 alloy journal bearings by experimental and means of artificial neural networks method. Industrial Lubrication and Tribology 64:5, pages 258-264.
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