Research Article

Modelling and Simulation of Machining Attributes in dry Turning of Aircraft Materials Nimonic C263 using CBN

¹ Department of Mechanical Engineering, RMK College of Engineering and Technology, Chennai, India
² Department of Mechanical Engineering, Shreenivasa Engineering College, Dharmapuri, India
³ Center for Materials Research, Chennai Institute of Technology, Chennai, India
⁴ Department of Mechanical Engineering, College of Electrical and Mechanical Engineering, Center of Excellence-Nano Technology, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
⁵ Department of mechanical Engineering, Prathyusha Engineering College, Chennai, India

^* e-mail: shiva@aastu.edu.et

Received: 23 August 2021
Accepted: 14 November 2021

Abstract

In the current scenario, machinability of the super alloys is of greater importance in an aircraft turbine engine and land-based turbine applications owing to its superior properties. However, the machinability of these alloys is found to be poor owing to its inherent properties. Hence, a predictive model has been developed based on DEFORM 3D to forecast the machining attributes such as cutting force and insert's cutting edge temperature in turning of Nimonic C263 super alloy. The dry turning trials on Nimonic C263 material were carried out based on L27 orthogonal array using CBN insert. Linear regression models were developed to predict the machining attributes. Further, multi response optimization was carried out based on desirability approach for optimizing the machining attributes. The validation test was carried out for optimal parameter values such as cutting speed: 117 m/min, feed rate: 0.055 mm/rev and depth of cut: 0.25 mm. The minimum cutting force of 304N and insert's cutting edge temperature of 468 °C were obtained at optimum level of parameters.The predicted values by FEA and linear regression model were compared with experimental results and found to be closer with minimum percentage error.The minimum percentage error obtained by FEA and linear regression model for the machining attributes (cutting force, temperature) as compared with experimental values were (0.32%, 0.23%) and (2.34%, 1.63%) respectively.