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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9320167 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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