Debabrata Dhupal

Experimental investigation on cutting force and surface roughness in machining of hardened AISI 52100 steel using cBN tool

The paper focused on finish dry hard turning of AISI 52100 steel with cBN tool by employing combined techniques (L9 OA and ANOVA) to determine the effect of cutting parameters (cutting speed, feed and depth of cut) on cutting force (Fc) and surface roughness (Ra, Rz). The results show that feed and cutting speed strongly influence surface roughness; whereas depth of cut is the principal significant factor affecting cutting force followed by feed. The prediction of optimal range (at 90% CI) for Fc, Ra and Rz, along with multi-response optimisation (based on desirability function approach) was performed in order to optimise the cutting parameters. Thereafter, the mathematical models for each response are developed using multiple linear regression analysis and several diagnostic tests have been performed to check the validity, effectiveness, adequacy of the developed model. Simultaneously, the tool flank wear pattern, machined surface of the workpiece and generated chips were microscopically examined under optimum cutting condition.

Parametric Optimization of Surface Roughness and Overcut in Electric Discharge Machining of Al-SiC Using Copper Electrode

Nowadays, Al-SiC metal matrix composites are replacing conventional materials in aerospace and automobile applications because of their superior characteristics. However, present manufacturers are facing various challenges during machining these advanced, hard-to-cut materials. One alternative method suitable in such situation is electrical discharge machining which employs series of discrete sparks that melt and vaporize the material irrespective of the hardness. The objective of present experimental investigation is to estimate and develop comprehensive mathematical models for selected performance parameters such as, surface roughness (Ra), and overcut (OC) with the five input machining parameters such as low voltage current, high voltage current, pulse-on time, pulse-off time and flushing pressure through response surface methodology. Thereafter, the significance of the developed models have been verified through analysis of variance. Results showed surface finish improves with the increasing pulse-off time and flushing pressure whereas, overcut decreases with the increasing high voltage current but reverse effect obtained with pulse-on time. In end of the discussion, a multi-objective particle swarm optimization (MOPSO) algorithm has been employed for simultaneous optimization of muli-responses which provides helpful guidance for governing the machining parameters to enhance accuracy of the electrical discharge machined components and a confirmation test is performed at the optimized parameter setting for copper and brass electrodes respectively to validate the result.