<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A low cost three axes commercial microEDM machine has been developed indigenously in order to implement precision micromachining. The developed microEDM consists of various components such as machining chamber, X-Y table, tool feed and tool holder assembly, dielectric circulation system, power supply and automatic driving devices to control the spark gap distance. The setup is capable of performing the 8µm movement in Z-axis.The developed 3 axes machine and the commercial available machine are investigated with the most important process parameters such as Current, Pulse on time and Pulse off time in order to study the Material Removal Rate (MRR) and Tool Wear Rate (TWR). The material chosen for the current research is Stainless Steel 316L, which has high corrosion resistance and the electrode was tungsten 300µm. In most of the experiments, the developed machine has performed with 6.52% higher MRR and 26.10% Lower TWR. This is due to the effective spark gap circuit developed with the use of hall current sensor.The mathematical model of MRR and TWR are obtained by correlating the process parameter using Response Surface Methodology (RSM).The chosen objectives are contradictory in nature, we employ a non-dominated Genetic algorithm to find the optimal process parameters. The developed 3 axes microEDM has yield a 10.14% and 33.12% better performance on optimum maximum MRR and minimum TWR respectively when compared to the commercial machine.</p>
</abstract>

A low cost three axes commercial microEDM machine has been developed indigenously in order to implement precision micromachining. The developed microEDM consists of various components such as machining chamber, X-Y table, tool feed and tool holder assembly, dielectric circulation system, power supply and automatic driving devices to control the spark gap distance. The setup is capable of performing the 8µm movement in Z-axis.The developed 3 axes machine and the commercial available machine are investigated with the most important process parameters such as Current, Pulse on time and Pulse off time in order to study the Material Removal Rate (MRR) and Tool Wear Rate (TWR). The material chosen for the current research is Stainless Steel 316L, which has high corrosion resistance and the electrode was tungsten 300µm. In most of the experiments, the developed machine has performed with 6.52% higher MRR and 26.10% Lower TWR. This is due to the effective spark gap circuit developed with the use of hall current sensor.The mathematical model of MRR and TWR are obtained by correlating the process parameter using Response Surface Methodology (RSM).The chosen objectives are contradictory in nature, we employ a non-dominated Genetic algorithm to find the optimal process parameters. The developed 3 axes microEDM has yield a 10.14% and 33.12% better performance on optimum maximum MRR and minimum TWR respectively when compared to the commercial machine.

Assessment on the Optimum Performance of a Novel 3 Axes Micro EDM Developed for Commercial Applications

Sona College of Technology, Department of Mechatronics Engineering

Strojnícky časopis - Journal of Mechanical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A low cost three axes commercial microEDM machine has been developed indigenously in order to implement precision micromachining. The developed microEDM...

Assessment on the Optimum Performance of a Novel 3 Axes Micro EDM Developed for Commercial Applications

Publicado en línea: 07 dic 2021

Páginas: 305 - 316

DOI: https://doi.org/10.2478/scjme-2021-0037

Palabras clave
Material Removal Rate, Tool Wear Rate, Multi-objective Optimization, Genetic Algorithm, 3 axes microEDM

© 2021 P Suresh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Assessment on the Optimum Performance of a Novel 3 Axes Micro EDM Developed for Commercial Applications

Publicado en línea: 07 dic 2021

Páginas: 305 - 316

DOI: https://doi.org/10.2478/scjme-2021-0037

Palabras claveMaterial Removal Rate, Tool Wear Rate, Multi-objective Optimization, Genetic Algorithm, 3 axes microEDM

© 2021 P Suresh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Material Removal Rate, Tool Wear Rate, Multi-objective Optimization, Genetic Algorithm, 3 axes microEDM