Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms
Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the proc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Prakash, Chander [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 |
|---|---|
| Übergeordnetes Werk: |
volume:101 ; year:2018 ; number:1-4 ; day:14 ; month:11 ; pages:1107-1123 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00170-018-3017-3 |
|---|
| Katalog-ID: |
OLC2026132968 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2026132968 | ||
| 003 | DE-627 | ||
| 005 | 20230323141940.0 | ||
| 007 | tu | ||
| 008 | 200820s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00170-018-3017-3 |2 doi | |
| 035 | |a (DE-627)OLC2026132968 | ||
| 035 | |a (DE-He213)s00170-018-3017-3-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 670 |q VZ |
| 100 | 1 | |a Prakash, Chander |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag London Ltd., part of Springer Nature 2018 | ||
| 520 | |a Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. | ||
| 650 | 4 | |a Gas tungsten arc welding | |
| 650 | 4 | |a Optimization | |
| 650 | 4 | |a Modeling | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Pulsed current | |
| 700 | 1 | |a Singh, Sunpreet |4 aut | |
| 700 | 1 | |a Singh, Manjeet |4 aut | |
| 700 | 1 | |a Gupta, Munish Kumar |4 aut | |
| 700 | 1 | |a Mia, Mozammel |4 aut | |
| 700 | 1 | |a Dhanda, Ankit |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The international journal of advanced manufacturing technology |d Springer London, 1985 |g 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 |w (DE-627)129185299 |w (DE-600)52651-4 |w (DE-576)014456192 |x 0268-3768 |7 nnns |
| 773 | 1 | 8 | |g volume:101 |g year:2018 |g number:1-4 |g day:14 |g month:11 |g pages:1107-1123 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00170-018-3017-3 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_2333 | ||
| 951 | |a AR | ||
| 952 | |d 101 |j 2018 |e 1-4 |b 14 |c 11 |h 1107-1123 | ||
| author_variant |
c p cp s s ss m s ms m k g mk mkg m m mm a d ad |
|---|---|
| matchkey_str |
article:02683768:2018----::utojcieaaercpriaoplecretatnseacednpoesys |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s00170-018-3017-3 doi (DE-627)OLC2026132968 (DE-He213)s00170-018-3017-3-p DE-627 ger DE-627 rakwb eng 670 VZ Prakash, Chander verfasserin aut Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current Singh, Sunpreet aut Singh, Manjeet aut Gupta, Munish Kumar aut Mia, Mozammel aut Dhanda, Ankit aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 https://doi.org/10.1007/s00170-018-3017-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 101 2018 1-4 14 11 1107-1123 |
| spelling |
10.1007/s00170-018-3017-3 doi (DE-627)OLC2026132968 (DE-He213)s00170-018-3017-3-p DE-627 ger DE-627 rakwb eng 670 VZ Prakash, Chander verfasserin aut Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current Singh, Sunpreet aut Singh, Manjeet aut Gupta, Munish Kumar aut Mia, Mozammel aut Dhanda, Ankit aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 https://doi.org/10.1007/s00170-018-3017-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 101 2018 1-4 14 11 1107-1123 |
| allfields_unstemmed |
10.1007/s00170-018-3017-3 doi (DE-627)OLC2026132968 (DE-He213)s00170-018-3017-3-p DE-627 ger DE-627 rakwb eng 670 VZ Prakash, Chander verfasserin aut Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current Singh, Sunpreet aut Singh, Manjeet aut Gupta, Munish Kumar aut Mia, Mozammel aut Dhanda, Ankit aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 https://doi.org/10.1007/s00170-018-3017-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 101 2018 1-4 14 11 1107-1123 |
| allfieldsGer |
10.1007/s00170-018-3017-3 doi (DE-627)OLC2026132968 (DE-He213)s00170-018-3017-3-p DE-627 ger DE-627 rakwb eng 670 VZ Prakash, Chander verfasserin aut Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current Singh, Sunpreet aut Singh, Manjeet aut Gupta, Munish Kumar aut Mia, Mozammel aut Dhanda, Ankit aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 https://doi.org/10.1007/s00170-018-3017-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 101 2018 1-4 14 11 1107-1123 |
| allfieldsSound |
10.1007/s00170-018-3017-3 doi (DE-627)OLC2026132968 (DE-He213)s00170-018-3017-3-p DE-627 ger DE-627 rakwb eng 670 VZ Prakash, Chander verfasserin aut Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current Singh, Sunpreet aut Singh, Manjeet aut Gupta, Munish Kumar aut Mia, Mozammel aut Dhanda, Ankit aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 https://doi.org/10.1007/s00170-018-3017-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 101 2018 1-4 14 11 1107-1123 |
| language |
English |
| source |
Enthalten in The international journal of advanced manufacturing technology 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 |
| sourceStr |
Enthalten in The international journal of advanced manufacturing technology 101(2018), 1-4 vom: 14. Nov., Seite 1107-1123 volume:101 year:2018 number:1-4 day:14 month:11 pages:1107-1123 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current |
| dewey-raw |
670 |
| isfreeaccess_bool |
false |
| container_title |
The international journal of advanced manufacturing technology |
| authorswithroles_txt_mv |
Prakash, Chander @@aut@@ Singh, Sunpreet @@aut@@ Singh, Manjeet @@aut@@ Gupta, Munish Kumar @@aut@@ Mia, Mozammel @@aut@@ Dhanda, Ankit @@aut@@ |
| publishDateDaySort_date |
2018-11-14T00:00:00Z |
| hierarchy_top_id |
129185299 |
| dewey-sort |
3670 |
| id |
OLC2026132968 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2026132968</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323141940.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00170-018-3017-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2026132968</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00170-018-3017-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Prakash, Chander</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gas tungsten arc welding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pulsed current</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Sunpreet</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Manjeet</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gupta, Munish Kumar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mia, Mozammel</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dhanda, Ankit</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of advanced manufacturing technology</subfield><subfield code="d">Springer London, 1985</subfield><subfield code="g">101(2018), 1-4 vom: 14. Nov., Seite 1107-1123</subfield><subfield code="w">(DE-627)129185299</subfield><subfield code="w">(DE-600)52651-4</subfield><subfield code="w">(DE-576)014456192</subfield><subfield code="x">0268-3768</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:101</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:1-4</subfield><subfield code="g">day:14</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1107-1123</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00170-018-3017-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2333</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">101</subfield><subfield code="j">2018</subfield><subfield code="e">1-4</subfield><subfield code="b">14</subfield><subfield code="c">11</subfield><subfield code="h">1107-1123</subfield></datafield></record></collection>
|
| author |
Prakash, Chander |
| spellingShingle |
Prakash, Chander ddc 670 misc Gas tungsten arc welding misc Optimization misc Modeling misc Algorithms misc Pulsed current Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| authorStr |
Prakash, Chander |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129185299 |
| format |
Article |
| dewey-ones |
670 - Manufacturing |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0268-3768 |
| topic_title |
670 VZ Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms Gas tungsten arc welding Optimization Modeling Algorithms Pulsed current |
| topic |
ddc 670 misc Gas tungsten arc welding misc Optimization misc Modeling misc Algorithms misc Pulsed current |
| topic_unstemmed |
ddc 670 misc Gas tungsten arc welding misc Optimization misc Modeling misc Algorithms misc Pulsed current |
| topic_browse |
ddc 670 misc Gas tungsten arc welding misc Optimization misc Modeling misc Algorithms misc Pulsed current |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
The international journal of advanced manufacturing technology |
| hierarchy_parent_id |
129185299 |
| dewey-tens |
670 - Manufacturing |
| hierarchy_top_title |
The international journal of advanced manufacturing technology |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 |
| title |
Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| ctrlnum |
(DE-627)OLC2026132968 (DE-He213)s00170-018-3017-3-p |
| title_full |
Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| author_sort |
Prakash, Chander |
| journal |
The international journal of advanced manufacturing technology |
| journalStr |
The international journal of advanced manufacturing technology |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
1107 |
| author_browse |
Prakash, Chander Singh, Sunpreet Singh, Manjeet Gupta, Munish Kumar Mia, Mozammel Dhanda, Ankit |
| container_volume |
101 |
| class |
670 VZ |
| format_se |
Aufsätze |
| author-letter |
Prakash, Chander |
| doi_str_mv |
10.1007/s00170-018-3017-3 |
| dewey-full |
670 |
| title_sort |
multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| title_auth |
Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| abstract |
Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| abstractGer |
Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 |
| container_issue |
1-4 |
| title_short |
Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms |
| url |
https://doi.org/10.1007/s00170-018-3017-3 |
| remote_bool |
false |
| author2 |
Singh, Sunpreet Singh, Manjeet Gupta, Munish Kumar Mia, Mozammel Dhanda, Ankit |
| author2Str |
Singh, Sunpreet Singh, Manjeet Gupta, Munish Kumar Mia, Mozammel Dhanda, Ankit |
| ppnlink |
129185299 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00170-018-3017-3 |
| up_date |
2024-07-04T03:11:38.255Z |
| _version_ |
1803616463902212096 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2026132968</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323141940.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00170-018-3017-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2026132968</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00170-018-3017-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Prakash, Chander</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-objective parametric appraisal of pulsed current gas tungsten arc welding process by using hybrid optimization algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gas tungsten arc welding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pulsed current</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Sunpreet</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Manjeet</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gupta, Munish Kumar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mia, Mozammel</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dhanda, Ankit</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of advanced manufacturing technology</subfield><subfield code="d">Springer London, 1985</subfield><subfield code="g">101(2018), 1-4 vom: 14. Nov., Seite 1107-1123</subfield><subfield code="w">(DE-627)129185299</subfield><subfield code="w">(DE-600)52651-4</subfield><subfield code="w">(DE-576)014456192</subfield><subfield code="x">0268-3768</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:101</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:1-4</subfield><subfield code="g">day:14</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1107-1123</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00170-018-3017-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2333</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">101</subfield><subfield code="j">2018</subfield><subfield code="e">1-4</subfield><subfield code="b">14</subfield><subfield code="c">11</subfield><subfield code="h">1107-1123</subfield></datafield></record></collection>
|
| score |
7.3988647 |