Analysis of Influence of micro-EDM Parameters on MRR, TWR and Ra in Machining Ni-Ti Shape Memory Alloy
Mohammed Sarvar Rasheed1, A. M. Al-Ahmari2, A. M. El-Tamimi3, Mustufa H. Abidi4
1Mohammed Sarvar Rasheed, Department of Industrial Engineering, King Saud University, College of Engineering, Advanced Manufacturing Institute, Riyadh, KSA.
2A. M. Al-Ahmari, Department of Industrial Engineering, King Saud University, College of Engineering, Advanced Manufacturing Institute, Riyadh, KSA
3A. M. El-Tamimi, Department of Industrial Engineering, King Saud University, College of Engineering, Advanced Manufacturing Institute, Riyadh, KSA.
4Mustufa H. Abidi, Department of Industrial Engineering, King Saud University, College of Engineering, Advanced Manufacturing Institute, Riyadh, KSA.
Manuscript received on 18 October 2012 | Revised Manuscript received on 25 October 2012 | Manuscript published on 30 October 2012 | PP: 32-37 | Volume-1 Issue-4, October 2012 | Retrieval Number: D0326091412/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the micro-machining and MEMS industry, micro-Electrical discharge machining (µ-EDM) is an important process. In this paper, the Taguchi design approach has been employed to investigate the micro-EDM parameters in order to achieve the highest Material Removal Rate (MRR), good surface quality and low Tool Wear Rate (TWR) while machining Ni-Ti based Shape Memory Alloy (SMA). Based on these investigations, it has been observed that MRR is highly influenced by capacitance, discharge voltage and depends upon electrode material. TWR and Ra were found to be better at low energy levels. Tungsten electrode is recommended for better surface roughness and brass electrode for better MRR. SEM images have been used to observe the dimensional accuracy of micro-holes produced.
Keywords: Micro-EDM, Micro-Holes, Ni-Ti Shape Memory Alloy.
Scope of the Article: Machine Learning