Experimental Investigations and Analysis of Electrical Discharge Machining Of Hardened EN31 Steel Using Cermet Tool Tip

Abstract

Nowadays, alloys with high strength at elevated temperature and hardened steels are extensively used. Traditional machining processes are not capable of machining these materials efficiently. Electric Discharge machining (EDM) is one of the advanced machining processes, where the intricate profiles on hard to cut materials with greater dimensional precision can be performed. In this process, repeated electric discharges between tool and work piece are used to remove material in the presence of dielectric. EDM can machine all materials which are electrically conductive, regardless the workpiece hardness. The EDM tool does not touch the work material directly, consequently eliminating complications of vibration, chatter and mechanical stresses during machining. The EDM process is also coupled with some challenges. The tool wear problem is very critical since the tool shape degeneration directly affects the final shape of the die cavity. In most EDM operations, the contribution of tool cost to the total operation cost is more than 70%. During the cut by EDM, the material removal rate (MRR) decreases due to process instability and change of metallurgic constituent in the machining zone. The surface machined with EDM occasionally has characteristics like debris globules, cracks, craters, pores and pockmarks. Creation of recast layer or white layer and surface cracks, surface roughness, residual stress and metallurgical changes of base material is used to characterize the surface integrity. In this work, an attempt has been made to overcome the problem of tool wear in the EDM process. To reduce electrode wear, the fabrication of modified tool, having altered mechanical, electrical and thermal properties, is suggested. Materials having high melting point and wear resistance properties, with good electrical and thermal conductivity are used to reduce the electrode wear of copper based electrodes. Titanium carbide, silicon carbide and graphite at various compositions have been added with copper to fabricate the tool tip by powder metallurgy (P/M) technique. The electrical and mechanical characterization of the tool tips has been done. On the basis of this, the tool tip with copper content of 75% and titanium carbide content of 25% has been selected. The performance of the newly fabricated cermet tool tip has been compared with conventional copper tool tip. The present work correlates the inter-relationships of discharge current, gap voltage, pulse on time, pulse off time and flushing pressure with electrode wear rate (EWR), material removal rate (MRR), surface roughness and change in out of roundness in EDM process. Regression models have been developed to predict EWR, MRR, surface roughness and change in out of roundness by correlating the input parameters. The significance of EDM parameters on the selected responses has been evaluated using analysis of variance (ANOVA). Confirmation experiments were also conducted at various test conditions to show that the developed models for EDM process can predict EWR, MRR, surface roughness and change in out of roundness values accurately within 99.9% confidence interval. It has been revealed that EWR and change in OOR was reduced significantly using cermet tool tip. The change in out of roundness also highlights that the shape retention is better in cermet tool tip as compared to the copper tool tip. The MRR is higher when cermet tool tip has been used for the same set of process parameters. On comparing the percentage rise of surface roughness of workpiece after EDM, it was revealed that cermet tool tip contributed lesser transfer of surface roughness as compared to conventional copper tool tip. In the present study, the surface integrity has also been explored. It has been noticed that with both types of tool tip, the machined surface is characterized by irregular fused structures, debris, globules, shallow pits, surface cracks and micropores. The surface machined with cermet tool tip has fewer surface cracks, lesser void volume and less thick recast layer. The mechanism by which material is removed for cermet tool tip is predominately due to melting and evaporation, and to some extent, oxidation and decomposition. The machined surface of the workpiece has a significant enrichment of carbon content with respect to substrate material. Wear rate regression equations based on machine learning has been developed and compared with the regression equation generated by DOE. The machine learning based regression equation predicted EWR with an average error of 4.24% in comparison to 6.13% for DOE based regression equation. During the prediction of MRR, the average error for machine learning and DOE model has been found to be 2.77% and 5.58% respectively. The machine learning regression based artificial neural network has been used for the evaluation of the wear rate of electrodes. The present study has demonstrated the improved machining performance and consequently reduced electrode wear, improved material removal and better surface integrity with substantial reduction in out of roundness of electrode by the use of cermet tool tip.