Finite element simulation and regression modeling of machining attributes on turning AISI 304 stainless steel

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

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

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

Experimental analysis of process parameters in drilling nimonic C263 alloy under nano fluid mixed MQL environment

Nimonic C263 is a super alloy and it is difficult to cut. As this alloy possess high proportion of chromium, cobalt, and molybdenum, which fortify the material by solution hardening, which inhibits the dislocation movement, resulting in higher plastic deformation. In this research, an attempt has been made to model, analysis and investigate the machining characteristics such as thrust force, temperature at drill cutting edge, flank wear and surface finish during drilling of this alloy using silver nano fluid mixed Minimum Quantity Lubrication (MQL) environment. Residual stress at various combinations of process parameters was also observed and discussed. RSM based empirical models of the process parameters and optimization of multi response was developed. Thrust force, Temperature at drill cutting edge, surface roughness and tool wear affected by feed rate (percentage of contribution-60%), spindle speed (percentage of contribution-88.63%), spindle speed (percentage of contribution-71.42%) and feed rate (percentage of contribution-67.76%) respectively followed by other parameters

Application of MOORA & COPRAS integrated with entropy method for multi-criteria decision making in dry turning process of Nimonic C263

This article presents an integrated multi-criteria decision making using Entropy, MOORA and COPRAS methods for turning Nimonic C263. Experiments were performed under L27 Taguchi orthogonal array. Dry turning was performed and cubic boron nitride (CBN) was used to turn the alloy. The variables speed, feed and depth of cut were chosen as factors. For each experiment, the shear force component was measured during on line. The insert flank wear was measured after every experiment. The main objective of this paper is to identify the suitable trial to ensure minimum force and flank wear simultaneously. Because of the cost reduction and quality improvement, the controlling factors level should be selected appropriately. Hence, the integrated MCDM technique using MOORA, COPRAS and Entropy was chosen to determine the best experiment out of 27 experiments. Alternatives were ranked and the results were evaluated. The best experiment for minimization of force and flank wear is found to be 125 m/min, 0.055 mm/rev and 0.25 mm. The experimental test were observed with lesser deviation and confirmed that proposal found is more suitable to obtain minimum force and flank wear