Prediction of Kerf Width and Surface Roughness of Al6351 Based Composite in Wire-Cut Electric Discharge Machining Using Mathematical Modelling

Abstract

The machining of composite materials has been an area of intense research for the past couple of decades due to its wide range of applications, from automobiles to air crafts or from boats to nuclear systems. Non-conventional machining, especially electric discharge machining (EDM), is found to be a good machining option for meeting the required outputs. To overcome the challenges of machining complex shapes, wire electric discharge machining (WEDM) was developed. Al6351 composites was observed to be extensively used in nuclear applications. Therefore, identifying the kerf width and surface roughness are important criteria for the dimensional accuracy of the final product. The present work aims at predicting the behavior of the two major machining parameters which are kerf width and surface roughness of Al6351 composites in wire EDM by creating a mathematical model using ANOVA for different combinations of the reinforcements and comparing the variations in the coefficients for different combinations of reinforcements. The developed model has been validated by conducting similar set of experiments in Al6351-5% SiC-1% B4C hybrid composite. From the work, it was identified that pulse on time and current are the major contributing factor for kerf width and wire feed rate was observed to be contributing to the surface roughness. The validation results show an average variation of 8.17% for kerf width and 11.27% for surface roughness. The work can be successfully utilized for prediction of the kerf width and surface roughness of the composites manufactured with Al6351 as the base matrix material.

Modeling and optimization of WEDM of monel 400 alloy using ANFIS and snake optimizer: A comparative study

“…Here, the FM and FF, which represent the fighting ability of the male and female snakes, are determined using equations ( 20) and (21).…”

“…Esme et al 20 recommended a factorial design and a neural network to evaluate the surface roughness of AISI 4340 alloy during WEDM machining, noting that the neural network performed significantly better than the factorial design technique. Ram et al 21 conducted a thorough analysis of WEDM factors in the ability to forecast the kerf width and surface roughness of the Al6351 alloy using an ANOVA approach. Phate et al 22 combined the TOPSIS and ANFIS methodologies to investigate the WEDM process parameters and used ANOVA to conduct a comparative study.…”

Electric Discharge Machining on Stainless Steel Using a Blend of Copper and Fly Ash as the Electrode Material

“…The residue is then flushed away by the dielectric medium. The various parameters that affect the results of EDM are the pulse current, servo voltage, pulse on time, pulse off time, dielectric medium, and electrode material [ 10 , 11 , 12 ].…”