Fuzzy based modeling and optimization of EDMed response of Zircaloy-2

Fuzzy based modeling and optimization of EDMed response of Zircaloy-2

In this work, fuzzy model was developed that predicts response parameters and surface properties of an electrical discharge machined Zircaloy-2. Taguchi L18 mixed design was used to perform the experiments using different process parameters (polarity, pulse-on-time, pulse-off-time, tool electrode ma...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Composites and advanced materials 2024-04, Vol.33
Hauptverfasser: Kumar, Jitendra, Soota, Tarun, Sunil, BD Y, Gupta, Nakul, Rajput, Sunil Kumar, Sachan, Prachi, Saxena, Kuldeep K, Jule, Leta Tesfaye
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title Composites and advanced materials
container_volume 33
creator Kumar, Jitendra
Soota, Tarun
Sunil, BD Y
Gupta, Nakul
Rajput, Sunil Kumar
Sachan, Prachi
Saxena, Kuldeep K
Jule, Leta Tesfaye
description In this work, fuzzy model was developed that predicts response parameters and surface properties of an electrical discharge machined Zircaloy-2. Taguchi L18 mixed design was used to perform the experiments using different process parameters (polarity, pulse-on-time, pulse-off-time, tool electrode material, and peak current). Material removal rate (MRR) and tool wear rate (TWR) were chosen as machining response parameters, whereas number of particles (NoP) and the percentage particle area (PPA) for surface properties of EDMed surface. Digital image processing tool was used to evaluate the surface properties. Fuzzy-Sugeno (FS)-model was developed to predict MRR, TWR, NoP, and PPA. Model accuracy was found to be 94% for MRR and TWR, and 92% for NoP and PPA. Maximum MRR 1.53 × 10−3 mm3/min found when machining was performed using graphite tool with negative polarity. Fuzzy Sugeno-GRA method was successfully implemented to predict optimal response corresponding to high value of GRG.
doi_str_mv 10.1177/26349833241249749
format Article
fullrecord <record><control><sourceid>sage_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1177_26349833241249749</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_26349833241249749</sage_id><sourcerecordid>10.1177_26349833241249749</sourcerecordid><originalsourceid>FETCH-LOGICAL-c209t-a88c3e8fb91170db78c4642a2f934942f782fba837c78c0f7e69aa03cec646ae3</originalsourceid><addsrcrecordid>eNp9kM9KxDAQxoMouKz7AN7yAl3zjyY5ytpVYcWLXryUaZosXdqmJN1D-_SmrAdB8DTDb-YbvvkQuqdkS6mUDyznQivOmaBMaCn0FVotLFvg9a_-Fm1iPBFCmCKcsHyFiv15nidcQbQ17nxt26Y_Yuhr7Iex6ZoZxsb32DtcPL2llWDj4PtoF_LVBAOtnzJ2h24ctNFufuoafe6Lj91Ldnh_ft09HjLDiB4zUMpwq1ylk21SV1IZkQsGzOn0gGBOKuYqUFyaNCJO2lwDEG6syUUOlq8Rvdw1wccYrCuH0HQQppKScomi_BNF0mwvmghHW578OfTJ4j-Cb14iXbQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Fuzzy based modeling and optimization of EDMed response of Zircaloy-2</title><source>Sage Journals GOLD Open Access 2024</source><creator>Kumar, Jitendra ; Soota, Tarun ; Sunil, BD Y ; Gupta, Nakul ; Rajput, Sunil Kumar ; Sachan, Prachi ; Saxena, Kuldeep K ; Jule, Leta Tesfaye</creator><creatorcontrib>Kumar, Jitendra ; Soota, Tarun ; Sunil, BD Y ; Gupta, Nakul ; Rajput, Sunil Kumar ; Sachan, Prachi ; Saxena, Kuldeep K ; Jule, Leta Tesfaye</creatorcontrib><description>In this work, fuzzy model was developed that predicts response parameters and surface properties of an electrical discharge machined Zircaloy-2. Taguchi L18 mixed design was used to perform the experiments using different process parameters (polarity, pulse-on-time, pulse-off-time, tool electrode material, and peak current). Material removal rate (MRR) and tool wear rate (TWR) were chosen as machining response parameters, whereas number of particles (NoP) and the percentage particle area (PPA) for surface properties of EDMed surface. Digital image processing tool was used to evaluate the surface properties. Fuzzy-Sugeno (FS)-model was developed to predict MRR, TWR, NoP, and PPA. Model accuracy was found to be 94% for MRR and TWR, and 92% for NoP and PPA. Maximum MRR 1.53 × 10−3 mm3/min found when machining was performed using graphite tool with negative polarity. Fuzzy Sugeno-GRA method was successfully implemented to predict optimal response corresponding to high value of GRG.</description><identifier>ISSN: 2634-9833</identifier><identifier>EISSN: 2634-9833</identifier><identifier>DOI: 10.1177/26349833241249749</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><ispartof>Composites and advanced materials, 2024-04, Vol.33</ispartof><rights>The Author(s) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c209t-a88c3e8fb91170db78c4642a2f934942f782fba837c78c0f7e69aa03cec646ae3</cites><orcidid>0000-0003-4064-5113</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/26349833241249749$$EPDF$$P50$$Gsage$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/26349833241249749$$EHTML$$P50$$Gsage$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,21966,27853,27924,27925,44945,45333</link.rule.ids></links><search><creatorcontrib>Kumar, Jitendra</creatorcontrib><creatorcontrib>Soota, Tarun</creatorcontrib><creatorcontrib>Sunil, BD Y</creatorcontrib><creatorcontrib>Gupta, Nakul</creatorcontrib><creatorcontrib>Rajput, Sunil Kumar</creatorcontrib><creatorcontrib>Sachan, Prachi</creatorcontrib><creatorcontrib>Saxena, Kuldeep K</creatorcontrib><creatorcontrib>Jule, Leta Tesfaye</creatorcontrib><title>Fuzzy based modeling and optimization of EDMed response of Zircaloy-2</title><title>Composites and advanced materials</title><description>In this work, fuzzy model was developed that predicts response parameters and surface properties of an electrical discharge machined Zircaloy-2. Taguchi L18 mixed design was used to perform the experiments using different process parameters (polarity, pulse-on-time, pulse-off-time, tool electrode material, and peak current). Material removal rate (MRR) and tool wear rate (TWR) were chosen as machining response parameters, whereas number of particles (NoP) and the percentage particle area (PPA) for surface properties of EDMed surface. Digital image processing tool was used to evaluate the surface properties. Fuzzy-Sugeno (FS)-model was developed to predict MRR, TWR, NoP, and PPA. Model accuracy was found to be 94% for MRR and TWR, and 92% for NoP and PPA. Maximum MRR 1.53 × 10−3 mm3/min found when machining was performed using graphite tool with negative polarity. Fuzzy Sugeno-GRA method was successfully implemented to predict optimal response corresponding to high value of GRG.</description><issn>2634-9833</issn><issn>2634-9833</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><recordid>eNp9kM9KxDAQxoMouKz7AN7yAl3zjyY5ytpVYcWLXryUaZosXdqmJN1D-_SmrAdB8DTDb-YbvvkQuqdkS6mUDyznQivOmaBMaCn0FVotLFvg9a_-Fm1iPBFCmCKcsHyFiv15nidcQbQ17nxt26Y_Yuhr7Iex6ZoZxsb32DtcPL2llWDj4PtoF_LVBAOtnzJ2h24ctNFufuoafe6Lj91Ldnh_ft09HjLDiB4zUMpwq1ylk21SV1IZkQsGzOn0gGBOKuYqUFyaNCJO2lwDEG6syUUOlq8Rvdw1wccYrCuH0HQQppKScomi_BNF0mwvmghHW578OfTJ4j-Cb14iXbQ</recordid><startdate>20240423</startdate><enddate>20240423</enddate><creator>Kumar, Jitendra</creator><creator>Soota, Tarun</creator><creator>Sunil, BD Y</creator><creator>Gupta, Nakul</creator><creator>Rajput, Sunil Kumar</creator><creator>Sachan, Prachi</creator><creator>Saxena, Kuldeep K</creator><creator>Jule, Leta Tesfaye</creator><general>SAGE Publications</general><scope>AFRWT</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-4064-5113</orcidid></search><sort><creationdate>20240423</creationdate><title>Fuzzy based modeling and optimization of EDMed response of Zircaloy-2</title><author>Kumar, Jitendra ; Soota, Tarun ; Sunil, BD Y ; Gupta, Nakul ; Rajput, Sunil Kumar ; Sachan, Prachi ; Saxena, Kuldeep K ; Jule, Leta Tesfaye</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c209t-a88c3e8fb91170db78c4642a2f934942f782fba837c78c0f7e69aa03cec646ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kumar, Jitendra</creatorcontrib><creatorcontrib>Soota, Tarun</creatorcontrib><creatorcontrib>Sunil, BD Y</creatorcontrib><creatorcontrib>Gupta, Nakul</creatorcontrib><creatorcontrib>Rajput, Sunil Kumar</creatorcontrib><creatorcontrib>Sachan, Prachi</creatorcontrib><creatorcontrib>Saxena, Kuldeep K</creatorcontrib><creatorcontrib>Jule, Leta Tesfaye</creatorcontrib><collection>Sage Journals GOLD Open Access 2024</collection><collection>CrossRef</collection><jtitle>Composites and advanced materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kumar, Jitendra</au><au>Soota, Tarun</au><au>Sunil, BD Y</au><au>Gupta, Nakul</au><au>Rajput, Sunil Kumar</au><au>Sachan, Prachi</au><au>Saxena, Kuldeep K</au><au>Jule, Leta Tesfaye</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy based modeling and optimization of EDMed response of Zircaloy-2</atitle><jtitle>Composites and advanced materials</jtitle><date>2024-04-23</date><risdate>2024</risdate><volume>33</volume><issn>2634-9833</issn><eissn>2634-9833</eissn><abstract>In this work, fuzzy model was developed that predicts response parameters and surface properties of an electrical discharge machined Zircaloy-2. Taguchi L18 mixed design was used to perform the experiments using different process parameters (polarity, pulse-on-time, pulse-off-time, tool electrode material, and peak current). Material removal rate (MRR) and tool wear rate (TWR) were chosen as machining response parameters, whereas number of particles (NoP) and the percentage particle area (PPA) for surface properties of EDMed surface. Digital image processing tool was used to evaluate the surface properties. Fuzzy-Sugeno (FS)-model was developed to predict MRR, TWR, NoP, and PPA. Model accuracy was found to be 94% for MRR and TWR, and 92% for NoP and PPA. Maximum MRR 1.53 × 10−3 mm3/min found when machining was performed using graphite tool with negative polarity. Fuzzy Sugeno-GRA method was successfully implemented to predict optimal response corresponding to high value of GRG.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1177/26349833241249749</doi><orcidid>https://orcid.org/0000-0003-4064-5113</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2634-9833
ispartof Composites and advanced materials, 2024-04, Vol.33
issn 2634-9833
2634-9833
language eng
recordid cdi_crossref_primary_10_1177_26349833241249749
source Sage Journals GOLD Open Access 2024
title Fuzzy based modeling and optimization of EDMed response of Zircaloy-2
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T08%3A00%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-sage_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fuzzy%20based%20modeling%20and%20optimization%20of%20EDMed%20response%20of%20Zircaloy-2&rft.jtitle=Composites%20and%20advanced%20materials&rft.au=Kumar,%20Jitendra&rft.date=2024-04-23&rft.volume=33&rft.issn=2634-9833&rft.eissn=2634-9833&rft_id=info:doi/10.1177/26349833241249749&rft_dat=%3Csage_cross%3E10.1177_26349833241249749%3C/sage_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_sage_id=10.1177_26349833241249749&rfr_iscdi=true