scholarly journals Hybrid optimal scheme for minimizing machining force and surface roughness in hard turning of AISI 52100 steel

2019 ◽  
Vol 11 (3) ◽  
pp. 19 ◽  
Author(s):  
P Umamaheswarrao ◽  
D Ranga Raju ◽  
K.N.S. Suman ◽  
B Ravi Sankar
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Optimal Scheme ◽  
Aisi 52100 ◽  
Machining Force ◽  
Aisi 52100 Steel

scholarly journals Application of TOPSIS for multi response optimization of Process Parameters in dry hard turning of AISI 52100 steel

2021 ◽  
Vol 13 (1) ◽  
pp. 211-224
Author(s):  
P. UMAMAHESWARRAO ◽  
D. RANGARAJU ◽  
K. N. S. SUMAN ◽  
B. RAVISANKAR
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Hard Turning ◽  
Rake Angle ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Workpiece Surface ◽  
Aisi 52100 ◽  
Machining Force ◽  
Aisi 52100 Steel

In the present work by employing the Technique for order of preference by similarity to ideal solution (TOPSIS) machining parameters optimization is performed with polycrystalline cubic boron nitride (PCBN) tools while AISI 52100 steel hard turning (HT). Based on the CCD of RSM, 32 experimental runs were performed by varying cutting speed, feed, depth of cut, nose radius, and negative rake angle to identify the optimal level of the process parameters. In this study, the multiple performance characteristics measured are machining force, surface roughness, and workpiece surface temperature. To ascertain the impact of cutting parameters on responses, Analysis of Variance (ANOVA) was deployed. An optimum combination of input process parameters for the multiple performance characteristics should be as follows: speed 200 rpm, feed 0.1 mm/rev, depth of cut 0.8 mm, nose radius 1.2 mm, and negative rake angle 45º leading to the value of optimum response variables machining force 561.163 N, Surface roughness 0.507μm and workpiece surface temperature 84.38°C.

scholarly journals Machining force comparison for surface defect hard turning and conventional hard turning of AISI 52100 steel

2021 ◽  
Vol 13 (3) ◽  
pp. 205-214
Author(s):  
P. U MAMAHESWARRAO ◽  
D. RANGARAJU ◽  
K. N. S. SUMAN ◽  
B. RAVISANKAR
Keyword(s):  
Hard Turning ◽  
Surface Defect ◽  
Cutting Speed ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 52100 ◽  
Machining Force ◽  
Aisi 52100 Steel ◽  
The Impact

In this article, a recently developed method called surface defect machining (SDM) for hard turning has been adopted and termed surface defect hard turning (SDHT). The main purpose of the present study was to explore the impact of cutting parameters like cutting speed, feed, depth of cut, and tool geometry parameters such as nose radius and negative rake angle of the machining force during surface defect hard turning (SDHT) of AISI 52100 steel in dry condition with Polycrystalline cubic boron nitride (PCBN) tool; and results were compared with conventional hard turning (CHT). Experimentation is devised and executed as per Central Composite Design (CCD) of Response Surface Methodology (RSM). Results reported that an average machining force was decreased by 22% for surface defect hard turning (SDHT) compared to conventional hard turning (CHT).

A Regression Model for Force and Surface Roughness Estimation during Hard Turning

2011 ◽  
Vol 299-300 ◽  
pp. 1167-1170 ◽  
Author(s):  
Gaurav Bartarya ◽  
S.K. Choudhury
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Goodness Of Fit ◽  
Cutting Parameters ◽  
Anova Analysis ◽  
Aisi 52100 ◽  
Aisi 52100 Steel ◽  
Factorial Design Of Experiments ◽  
Full Factorial ◽  
Surface Roughness Estimation

Forces in Hard turning can be used to evaluate the performance of the process. Cutting parameters have their own influence on the cutting forces on the tool. The present work is an attempt to develop a force prediction model based on full factorial design of experiments for machining EN31 steel (equivalent to AISI 52100 steel) using uncoated CBN tool. The force and surface roughness regression models were developed using the data from various set of experiments with in the range of parameters selected. The predictions from the models were compared with the measured force and surface roughness values. The ANOVA analysis was undertaken to test the goodness of fit of data.

scholarly journals Surface Integrity of AISI 52100 Steel during Hard Turning in Different Near-Dry Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ajay Chavan ◽  
Vikas Sargade
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
Hard Turning ◽  
Vortex Tube ◽  
White Layer ◽  
Bearing Steel ◽  
Hybrid Nanofluid ◽  
Aisi 52100 ◽  
Aisi 52100 Steel ◽  
Cooling And Lubrication

AISI 52100 hardened bearing steel is popular in many industrial applications due to its excellent wear resistance and high strength. Therefore, a high level of surface integrity of the same is the utmost important requirement to enhance fatigue life. Machining of hardened AISI 52100 steel is difficult because severe plastic deformation and generation of high temperature alter the surface metallurgy of the machined component and hamper the tool life. The present investigation includes a comparative analysis of surface integrity of AISI 52100 bearing steel during hard turning under different near-dry environments, namely, dry, Minimum Quantity Cooling and Lubrication (MQCL), Compressed Chilled Air by Vortex Tube (CCAVT), and Hybrid Nanofluid Minimum Quantity Cooling and Lubrication (Hybrid NF-MQCL). Soyabean (a vegetable) oil is used as cutting fluid in MQCL and base fluid in Hybrid NF-MQCL environments. To prepare hybrid nanofluid, two different nanoparticles Al2O3 and MWCNT, are used. The chilled air is generated through a vortex tube. The surface integrity of AISI 52100 steel was studied in terms of microhardness, the thickness of the white layer, surface roughness (Ra), and residual stresses. Higher cutting speed and feed show positive and negative correlation on surface integrity of AISI 52100 steel, respectively. Hybrid nanofluid MQCL exhibits the lowest surface roughness (0.34 μm), microhardness (625 Hv0.1), compressive residual stresses (−168 MPa), and thin white layer (0.9 μm) in contrast, and dry machining shows higher surface roughness, microhardness, tensile residual stress, and thick white layer. In comparison, MQCL and CCAVT are found to be intermediate. It is found that hybrid nanofluid MQCL enhances the overall performance of the machined surface as compared to other near-dry techniques.

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