Effect of MQL on roughness, white layer and microhardness in hard turning of AISI 52100

Abstract

The influence of turning parameters on the surface integrity of the workpiece in hard turning of AISI 52100 steel using minimum quantity lubrication (MQL), was investigated in this work. The cutting parameters considered were cutting speed, feed, depth of cut and tool nose radius, at three levels each. Response surface methodology was employed to study the influence of factors on surface roughness, microhardness, topography, white layer thickness and elemental composition. From model analysis, the microhardness and surface roughness were best predicted by quadratic regression models, whereas white layer thickness was best evaluated by a linear model. The variance analysis revealed that surface roughness was significantly affected by nose radius (33·83%), followed by feed (32·5%). It was observed that surface defects were reduced with an increase in cutting speed. Microhardness was majorly affected by feed (36·64%), followed by nose radius (32·88%). The white layer thickness ranged from 4·3 to 15·9 μm for all experiments. White layer thickness was significantly influenced by nose radius (82·24%), cutting speed (9·78%) and feed (1·23%). Energy-dispersive X-ray spectroscopy analysis of the machined workpiece at a cutting speed of 150 m/min validated that oxidation was instigated and the weight percent of carbon was augmented in the base metal.

Cryogenic minimum quantity lubrication machining: from mechanism to application

“…25(b). The turning tool can always remain in a low-temperature state, indirectly cooling the cutting zone and reducing the use of LN 2 , which meets the requirements of green machining [117].…”

Performance analysis of cryogenically treated plus tempered carbide inserts in turning of Inconel 718 using cryogenic minimum quantity lubrication cooling technique

“…This forms the controlled lubrication at the machining interface. According to previous studies 44 and manufacturer's catalogue, mineral oil with a pressure of 6 bars and flow rate of 150 ml/h was used in the experiment.…”

Optimization for Machining Parameters in Turning of AISI 52100 Steel for Manufacturing Services-Genetic Algorithm Method

“…[1] Hence it is dire necessary to find out suitable parameters during machining.Several parameters like tool nose radius, feed rate, depth of cut, cutting velocity etc., determine the performance of machining. In the recent years, lot of attention is paid towards the influence of the above parameters over Output responses like cutting force, Material Removal Rate (MRR), Surface finish, Tool wear etc [2,3]. Attensioa et al performed machining on hardened AISI 52100 steel to study the influence of cutting speed and feed on material removal using PCBN inserts.…”