Abstract
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.
Highlights
One of the most important machining operations is turning, which is normally deployed in the aircraft manufacturing and automotive industries, among other areas
Ay [16] looked into the effects of machining parameters on surface roughness and cutting force while turning AISI 304L stainless steel and discovered the best machining conditions
The frictional force in the Coulomb law model is given in the equation: Data such as process parameters and conditions, insert material, environment temperature, work piece materials, cutting speed, depth of cut, feed rate, and other assumptions were used as input data in the modeling and simulation
Summary
One of the most important machining operations is turning, which is normally deployed in the aircraft manufacturing and automotive industries, among other areas. Sadeghifar et al [13] have reviewed the finite element orthogonal operations and the impact of the machining parameters on the machining characteristics such as tool wear, cutting force, and surface roughness They have commonly observed that, additional efforts are needed due to the increased complexity of the machining process, caused due to nonlinearities in the material and machining process. Ay [16] looked into the effects of machining parameters on surface roughness and cutting force while turning AISI 304L stainless steel and discovered the best machining conditions They have stated that, the wiper insert have achieved better efficiency well PVD TiAlN-coated conventional. The whale optimization algorithm was used by Tanvit et al [17] to optimise the machining parameters in turning the stainless steel 304 They have investigated the impact of machining parameters on cutting forces, peak tool temperature, surface roughness and material removal rate. The response surface methodology was used to develop a model to predict the responses
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