Volume 8, 2021
|Number of page(s)||11|
|Published online||17 August 2021|
Finite element simulation and regression modeling of machining attributes on turning AISI 304 stainless steel
Department of Mechanical Engineering, SRM Institute of Science and Technology (SRM IST) Ramapuram Campus, Chennai 600089, India
2 Department of Mechanical Engineering, Shreenivasa Engineering College, Dharmapuri, 635301, Tamil Nadu, India
3 Department of Mechanical engineering, Easwari Engineering College, Chennai 600089, India
4 Center for Materials Research, Chennai Institute of Technology, Chennai 600069, India
5 Department of Mechanical Engineering, Prathyusha Engineering College, Chennai 602025, India
* e-mail: firstname.lastname@example.org
Accepted: 13 July 2021
To-date, the usage of finite element analysis (FEA) in the area of machining operations has demonstrated to be efficient to investigate the machining processes. The simulated results have been used by tool makers and researchers to optimize the process parameters. As a 3D simulation normally would require more computational time, 2D simulations have been popular choices. In the present article, a Finite Element Model (FEM) using DEFORM 3D is presented, which was used to predict the cutting force, temperature at the insert edge, effective stress during turning of AISI 304 stainless steel. The simulated results were compared with the experimental results. The shear friction factor of 0.6 was found to be best, with strong agreement between the simulated and experimental values. As the cutting speed increased from 125 m/min to 200 m/min, a maximum value of 750 MPa stress as well as a temperature generation of 650 °C at the insert edge have been observed at rather higher feed rate and perhaps a mid level of depth of cut. Furthermore, the Response Surface Methodology (RSM) model is developed to predict the cutting force and temperature at the insert edge.
Key words: AISI 304 stainless steel / DEFORM 3D / simulation / cutting force / temperature at insert edge / effective stress
© A. Mathivanan et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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