Journal Published Online: 01 May 2008
Volume 36, Issue 4

Influence of Friction in Simple Upsetting and Prediction of Hardness Distribution in a Cold Forged Product

CODEN: JTEVAB

Abstract

Predicting inhomogeneous deformation in any forging process will definitely be helpful in deciding the tool, billet material, lubrication, annealing sequences, and number of stages to make products. In this work, the influence of varied friction conditions on the hardness and effective strain variation during simple upsetting is studied. Also, hardness variation in a typical cold forging process is predicted by relating hardness and effective strain evolution in a simple upsetting operation empirically. Four different lubricants, viz., castor oil (m=0.33), soap (m=0.25), grease (m=0.2), teflon (m=0.16), are considered for experimentation. The friction factors of these lubricants were obtained from a Ring Compression Test (RCT) and are used in FE simulations of upsetting and forging operations. It is found from the analyses that: (1) Teflon shows relatively less variation in hardness and effective strain depicting homogeneous upsetting operation, whereas other lubricants show a larger variation in hardness and effective strain in radial and axial directions; (2) hardness is observed to vary linearly with effective strain; (3) the empirical relationship between hardness and effective strain obtained from a simple upsetting operation, which is common for all the lubricants, predicts the hardness distribution during the forging-extrusion process with moderate accuracy. This depends on the interface friction conditions, i.e., solid and semi-solid lubricants with better holdability like Teflon and soap show good correlation between experimental and predicted hardness values than liquid lubricant, i.e., castor oil.

Author Information

Ganesh Narayanan, R.
Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India
Gopal, M.
Division of General Engineering, DMI College of Engineering, Chennai, India
Rajadurai, A.
Department of Production Engineering, Madras Institute of Technology, Chennai, India
Pages: 13
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Stock #: JTE101443
ISSN: 0090-3973
DOI: 10.1520/JTE101443