Title: Application of artificial neural network on deformation and densification behaviour of sintered Fe-C steel under cold upsetting

Authors: T.K. Kandavel; T. Ashok Kumar; D. Vijay; S. Ashwanth Samraj

Addresses: School of Mechanical Engineering, Shanmugha Arts, Science, Technology and Research Academy (SASTRA University), Thanjavur, Tamil Nadu – 613401, India ' School of Mechanical Engineering, Shanmugha Arts, Science, Technology and Research Academy (SASTRA University), Thanjavur, Tamil Nadu – 613401, India ' School of Mechanical Engineering, Shanmugha Arts, Science, Technology and Research Academy (SASTRA University), Thanjavur, Tamil Nadu – 613401, India ' School of Mechanical Engineering, Shanmugha Arts, Science, Technology and Research Academy (SASTRA University), Thanjavur, Tamil Nadu – 613401, India

Abstract: Cold upsetting is one of the densification processes used in Podwer Metallurgy (P/M) materials to achieve the desired density by applying required amount of load. The present work aims to study the deformation and densification characteristics of plain carbon steel (Fe-C) containing various levels of carbon viz. 0.2%, 0.5% and 1% under cold upsetting. The sintered preforms of various compositions of Fe-C were subjected to cold upset. The axial and lateral deformations were calculated from the physical measurements taken from the deformed and non-deformed specimens and the density of the deformed preforms was measured by Archimedes' principle. The experimental data were used further to generate the deformation and densification model using Artificial Neural Network (ANN). It is observed from the experimental results that increasing carbon content improves the deformation and densification properties of iron material as it behaves like a lubricant and increases the binding strength between the grains.

Keywords: artificial neural network; ANN; powder metallurgy; densification; deformation; true axial stress; plain carbon steel.

DOI: 10.1504/IJAIP.2019.098576

International Journal of Advanced Intelligence Paradigms, 2019 Vol.12 No.3/4, pp.266 - 278

Received: 01 Jun 2016
Accepted: 30 Nov 2016
Published online: 28 Mar 2019 *

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