The effects of electrode size and discharged power on micro-electro-discharge machining drilling of stainless steel

Gianluca D’Urso,Chiara Ravasio

doi:10.1177/1687814016648646

Gianluca D’Urso, Chiara Ravasio

Open Access

https://doi.org/10.1177/1687814016648646

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Journal: Advances in Mechanical Engineering	Publication Date: May 1, 2016
Citations: 11	License type: cc-by

Affiliation: University of Bergamo

Abstract

This article is about the measurement of actual micro-electro-discharge machining parameters and the statistical analysis of their influence on the process performances. In particular, the discharged power was taken into account as a comprehensive variable able to represent the effect of peak current and voltage on the final result. Thanks to the dedicated signal acquisition system, a correlation among the discharged power and the indexes representing the process parameters was shown. Finally, linear and non-linear regression approaches were implemented in order to obtain predictive equations for the most important aspects of micro-electro-discharge machining, such as the machining time and the electrode wear.

Highlights

Micro-electro-discharge machining is a promising technology able to remove material from the workpiece by means of electrical discharges
The energy level has a strong effect on MRR: using the low value the specific discharged power has a modest effect on MRR for both electrode diameters
The initial data set variables considered here for the regression are composed of electrode nominal diameter (D), energy level (E), peak current (I), peak voltage (V), energy per spark (E/spark), energy per second (E/s), total number of sparks (S), number of sparks per second (S/s), as input parameters and tool wear, machining time, hole top diameter and hole bottom diameter as dependent variables

Summary

Introduction

Micro-electro-discharge machining (micro-EDM) is a promising technology able to remove material from the workpiece by means of electrical discharges. Different values of the electrical parameters were set by varying peak current, voltage and energy level. A regression approach was implemented in order to obtain predictive equations for the most important indicators of micro-EDM, such as the machining time and the electrode wear.

Results

Conclusion