Investigation of electrochemical machining characteristics of 20MnCr5 alloy steel using potassium dichromate mixed aqueous NaCl electrolyte and optimization of process parameters

Author:

Ayyappan Solaiyappan1,Sivakumar Karuppan2

Affiliation:

1. Department of Mechanical Engineering, Government College of Engineering, Salem-636011, India

2. Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam-638401, India

Abstract

Electrochemical machining is a modern machining technique that plays an important role in the applications of aerospace, die and electronic industries. Electrochemical machining is used to machine difficult-to-machine materials and complex shapes. Machining of alloy steels especially 20MnCr5 is very important for its wide variety of applications such as piston bolts, spindles, camshafts, gears, shafts and other mechanical controlling parts. Machining of the above components with conventional machine tools is a burdensome task. Hence, in this work, an investigation was made to study the electrochemical machining characteristics of 20MnCr5 alloy steel. Two electrolytes, namely, aqueous sodium chloride (NaCl) and potassium dichromate (K2Cr2O7) mixed aqueous NaCl, were used to investigate the machining performance. K2Cr2O7 was selected for its oxidizing characteristics and was included in small proportions in aqueous NaCl bath. The influence of predominant electrochemical machining process parameters such as applied voltage (V), inter electrode gap and electrolyte concentration was studied on the material removal rate and surface roughness (Ra). Scanning electron microscope photography of surface of the 20MnCr5 specimen machined with electrochemical machining was studied to understand the effect of electrolytes during the machining. The contour plots were generated to study the effect of process parameters as well as their interactions. It was noted in the study that the presence of K2Cr2O7 in aqueous NaCl electrolyte increases the material removal rate significantly. The process parameters are optimized through genetic algorithm-desirability function. Optimized operating conditions were found to be quite close with experimental results.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

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