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Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for t...

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Autores principales: Chaudhari, Rakesh, Kevalramani, Aniket, Vora, Jay, Khanna, Sakshum, Patel, Vivek K., Pimenov, Danil Yurievich, Giasin, Khaled
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320167/
https://www.ncbi.nlm.nih.gov/pubmed/35888844
http://dx.doi.org/10.3390/mi13071026
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author Chaudhari, Rakesh
Kevalramani, Aniket
Vora, Jay
Khanna, Sakshum
Patel, Vivek K.
Pimenov, Danil Yurievich
Giasin, Khaled
author_facet Chaudhari, Rakesh
Kevalramani, Aniket
Vora, Jay
Khanna, Sakshum
Patel, Vivek K.
Pimenov, Danil Yurievich
Giasin, Khaled
author_sort Chaudhari, Rakesh
collection PubMed
description Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (T(on)), and pulse-off-time (T(off)) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and T(on) were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm(3)/s at T(on) of 95 µs, T(off) of 9 µs, current of 6 A. Least SR was obtained at T(on) of 35 µs, T(off) of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.
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spelling pubmed-93201672022-07-27 Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy Chaudhari, Rakesh Kevalramani, Aniket Vora, Jay Khanna, Sakshum Patel, Vivek K. Pimenov, Danil Yurievich Giasin, Khaled Micromachines (Basel) Article Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (T(on)), and pulse-off-time (T(off)) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and T(on) were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm(3)/s at T(on) of 95 µs, T(off) of 9 µs, current of 6 A. Least SR was obtained at T(on) of 35 µs, T(off) of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable. MDPI 2022-06-28 /pmc/articles/PMC9320167/ /pubmed/35888844 http://dx.doi.org/10.3390/mi13071026 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chaudhari, Rakesh
Kevalramani, Aniket
Vora, Jay
Khanna, Sakshum
Patel, Vivek K.
Pimenov, Danil Yurievich
Giasin, Khaled
Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy
title Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy
title_full Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy
title_fullStr Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy
title_full_unstemmed Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy
title_short Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy
title_sort parametric optimization and influence of near-dry wedm variables on nitinol shape memory alloy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320167/
https://www.ncbi.nlm.nih.gov/pubmed/35888844
http://dx.doi.org/10.3390/mi13071026
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