<abstract xmlns="http://www.w3.org/1999/xhtml">Nickel Titanium (NiTi) alloys are the class of smart materials classified under shape memory alloys. The traditional machining of these alloys is hard because of various inherent mechanical characteristics of these alloys. Therefore, non-traditional machining process such as wire electro discharge machining (WEDM) has been employed for machining of such alloys. The present study is focused on multi-performance characteristic simultaneous optimization of WEDM process parameters, in which three system control factors, namely, pulse on time (TON), pulse off time (TOFF) and wire feed (WF) are considered for simultaneously maximizing material removal rate (MRR), while minimizing surface roughness (SR) and tool wear rate (TWR). The simultaneous optimization is performed using Taguchi’s Quality Loss Function. Analysis of means and analysis of variance have been carried out to identify the significance level of each system control factor. The different levels of settings and the optimized setting have been analysed using scanning electron microscope images for surface morphological studies. The multi-response optimization investigations revealed that TON is the major contributing factor and optimal performance values were obtained at TON of 125μs, TOFF of 25μs and at WF of 4 m/min.</abstract>

Nickel Titanium (NiTi) alloys are the class of smart materials classified under shape memory alloys. The traditional machining of these alloys is hard because of various inherent mechanical characteristics of these alloys. Therefore, non-traditional machining process such as wire electro discharge machining (WEDM) has been employed for machining of such alloys. The present study is focused on multi-performance characteristic simultaneous optimization of WEDM process parameters, in which three system control factors, namely, pulse on time (TON), pulse off time (TOFF) and wire feed (WF) are considered for simultaneously maximizing material removal rate (MRR), while minimizing surface roughness (SR) and tool wear rate (TWR). The simultaneous optimization is performed using Taguchi’s Quality Loss Function. Analysis of means and analysis of variance have been carried out to identify the significance level of each system control factor. The different levels of settings and the optimized setting have been analysed using scanning electron microscope images for surface morphological studies. The multi-response optimization investigations revealed that TON is the major contributing factor and optimal performance values were obtained at TON of 125μs, TOFF of 25μs and at WF of 4 m/min.

Optimization of Wire EDM Process Parameters for Medical Grade Nickel Titanium Shape Memory Alloy

Advanced Manufacturing Laboratory, Department of Mechanical Engineering

Strojnícky časopis - Journal of Mechanical Engineering

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

Nickel Titanium (NiTi) alloys are the class of smart materials classified under shape memory alloys. The traditional machining of these alloys is hard because...

Optimization of Wire EDM Process Parameters for Medical Grade Nickel Titanium Shape Memory Alloy

Pubblicato online: 13 mag 2020

Pagine: 69 - 80

DOI: https://doi.org/10.2478/scjme-2020-0007

Parole chiave
Nickel Titanium, shape memory alloys, Simultaneous optimization, Quality loss function

© 2020 Vinayak N Kulkarni et al., published by Sciendo

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

Optimization of Wire EDM Process Parameters for Medical Grade Nickel Titanium Shape Memory Alloy

Pubblicato online: 13 mag 2020

Pagine: 69 - 80

DOI: https://doi.org/10.2478/scjme-2020-0007

Parole chiaveNickel Titanium, shape memory alloys, Simultaneous optimization, Quality loss function

© 2020 Vinayak N Kulkarni et al., published by Sciendo

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

Parole chiave
Nickel Titanium, shape memory alloys, Simultaneous optimization, Quality loss function