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Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach
The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicate...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456530/ https://www.ncbi.nlm.nih.gov/pubmed/37630072 http://dx.doi.org/10.3390/mi14081536 |
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author | Ishfaq, Kashif Sana, Muhammad Waseem, Muhammad Umair Ashraf, Waqar Muhammad Anwar, Saqib Krzywanski, Jaroslaw |
author_facet | Ishfaq, Kashif Sana, Muhammad Waseem, Muhammad Umair Ashraf, Waqar Muhammad Anwar, Saqib Krzywanski, Jaroslaw |
author_sort | Ishfaq, Kashif |
collection | PubMed |
description | The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 μm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC’s nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments. |
format | Online Article Text |
id | pubmed-10456530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104565302023-08-26 Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach Ishfaq, Kashif Sana, Muhammad Waseem, Muhammad Umair Ashraf, Waqar Muhammad Anwar, Saqib Krzywanski, Jaroslaw Micromachines (Basel) Article The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 μm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC’s nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments. MDPI 2023-07-31 /pmc/articles/PMC10456530/ /pubmed/37630072 http://dx.doi.org/10.3390/mi14081536 Text en © 2023 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 Ishfaq, Kashif Sana, Muhammad Waseem, Muhammad Umair Ashraf, Waqar Muhammad Anwar, Saqib Krzywanski, Jaroslaw Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach |
title | Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach |
title_full | Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach |
title_fullStr | Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach |
title_full_unstemmed | Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach |
title_short | Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach |
title_sort | enhancing edm machining precision through deep cryogenically treated electrodes and ann modelling approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456530/ https://www.ncbi.nlm.nih.gov/pubmed/37630072 http://dx.doi.org/10.3390/mi14081536 |
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