Aging process optimization for a copper alloy considering hardness and electrical conductivity
A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algor...
Gespeichert in:
Veröffentlicht in: | Computational materials science 2007-02, Vol.38 (4), p.697-701 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 701 |
---|---|
container_issue | 4 |
container_start_page | 697 |
container_title | Computational materials science |
container_volume | 38 |
creator | Su, Juan-hua Li, He-jun Liu, Ping Dong, Qi-ming Li, Ai-jun |
description | A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters. |
doi_str_mv | 10.1016/j.commatsci.2006.04.013 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_29269888</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0927025606001108</els_id><sourcerecordid>29269888</sourcerecordid><originalsourceid>FETCH-LOGICAL-c376t-df31520a6c7b209c029926bb10411698165d586a96b6629de5c377275b1514063</originalsourceid><addsrcrecordid>eNqFkM1OwzAQhC0EEqXwDOQCt4S1mzj2sar4k5C4wBXLsZ3iKomD7VYqT4-jVnDktF5pvlnPIHSNocCA6d2mUK7vZQzKFgSAFlAWgBcnaIZZzXNggE_RDDipcyAVPUcXIWwgkZyRGfpYru2wzkbvlAkhc2O0vf2W0boha53PZKbcOJr06Dq3T8sQrDZ-Yj6l18MEyUFnpjMqeqtkN2n0VkW7s3F_ic5a2QVzdZxz9P5w_7Z6yl9eH59Xy5dcLWoac90ucEVAUlU3BLgCwjmhTYOhxNNHMa10xajktKGUcG2qxNWkrhpc4RLoYo5uD74pyNfWhCh6G5TpOjkYtw2CJDvOGEvC-iBU3oXgTStGb3vp9wKDmPoUG_Hbp5j6FFCK1Gcib44nZEgxWy8HZcMfzkpWAS-TbnnQmZR3Z40XyckMymjrU0dCO_vvrR-EvJBe</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>29269888</pqid></control><display><type>article</type><title>Aging process optimization for a copper alloy considering hardness and electrical conductivity</title><source>Elsevier ScienceDirect Journals</source><creator>Su, Juan-hua ; Li, He-jun ; Liu, Ping ; Dong, Qi-ming ; Li, Ai-jun</creator><creatorcontrib>Su, Juan-hua ; Li, He-jun ; Liu, Ping ; Dong, Qi-ming ; Li, Ai-jun</creatorcontrib><description>A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters.</description><identifier>ISSN: 0927-0256</identifier><identifier>EISSN: 1879-0801</identifier><identifier>DOI: 10.1016/j.commatsci.2006.04.013</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Aging parameter optimization ; Cross-disciplinary physics: materials science; rheology ; Cu–Cr–Zr–Mg alloy ; Electrical conductivity ; Exact sciences and technology ; Hardness ; Materials science ; Physics ; Solid solution, precipitation, and dispersion hardening; aging ; Treatment of materials and its effects on microstructure and properties</subject><ispartof>Computational materials science, 2007-02, Vol.38 (4), p.697-701</ispartof><rights>2006 Elsevier B.V.</rights><rights>2007 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-df31520a6c7b209c029926bb10411698165d586a96b6629de5c377275b1514063</citedby><cites>FETCH-LOGICAL-c376t-df31520a6c7b209c029926bb10411698165d586a96b6629de5c377275b1514063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.commatsci.2006.04.013$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18485094$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Su, Juan-hua</creatorcontrib><creatorcontrib>Li, He-jun</creatorcontrib><creatorcontrib>Liu, Ping</creatorcontrib><creatorcontrib>Dong, Qi-ming</creatorcontrib><creatorcontrib>Li, Ai-jun</creatorcontrib><title>Aging process optimization for a copper alloy considering hardness and electrical conductivity</title><title>Computational materials science</title><description>A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters.</description><subject>Aging parameter optimization</subject><subject>Cross-disciplinary physics: materials science; rheology</subject><subject>Cu–Cr–Zr–Mg alloy</subject><subject>Electrical conductivity</subject><subject>Exact sciences and technology</subject><subject>Hardness</subject><subject>Materials science</subject><subject>Physics</subject><subject>Solid solution, precipitation, and dispersion hardening; aging</subject><subject>Treatment of materials and its effects on microstructure and properties</subject><issn>0927-0256</issn><issn>1879-0801</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNqFkM1OwzAQhC0EEqXwDOQCt4S1mzj2sar4k5C4wBXLsZ3iKomD7VYqT4-jVnDktF5pvlnPIHSNocCA6d2mUK7vZQzKFgSAFlAWgBcnaIZZzXNggE_RDDipcyAVPUcXIWwgkZyRGfpYru2wzkbvlAkhc2O0vf2W0boha53PZKbcOJr06Dq3T8sQrDZ-Yj6l18MEyUFnpjMqeqtkN2n0VkW7s3F_ic5a2QVzdZxz9P5w_7Z6yl9eH59Xy5dcLWoac90ucEVAUlU3BLgCwjmhTYOhxNNHMa10xajktKGUcG2qxNWkrhpc4RLoYo5uD74pyNfWhCh6G5TpOjkYtw2CJDvOGEvC-iBU3oXgTStGb3vp9wKDmPoUG_Hbp5j6FFCK1Gcib44nZEgxWy8HZcMfzkpWAS-TbnnQmZR3Z40XyckMymjrU0dCO_vvrR-EvJBe</recordid><startdate>20070201</startdate><enddate>20070201</enddate><creator>Su, Juan-hua</creator><creator>Li, He-jun</creator><creator>Liu, Ping</creator><creator>Dong, Qi-ming</creator><creator>Li, Ai-jun</creator><general>Elsevier B.V</general><general>Elsevier Science</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20070201</creationdate><title>Aging process optimization for a copper alloy considering hardness and electrical conductivity</title><author>Su, Juan-hua ; Li, He-jun ; Liu, Ping ; Dong, Qi-ming ; Li, Ai-jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-df31520a6c7b209c029926bb10411698165d586a96b6629de5c377275b1514063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Aging parameter optimization</topic><topic>Cross-disciplinary physics: materials science; rheology</topic><topic>Cu–Cr–Zr–Mg alloy</topic><topic>Electrical conductivity</topic><topic>Exact sciences and technology</topic><topic>Hardness</topic><topic>Materials science</topic><topic>Physics</topic><topic>Solid solution, precipitation, and dispersion hardening; aging</topic><topic>Treatment of materials and its effects on microstructure and properties</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Juan-hua</creatorcontrib><creatorcontrib>Li, He-jun</creatorcontrib><creatorcontrib>Liu, Ping</creatorcontrib><creatorcontrib>Dong, Qi-ming</creatorcontrib><creatorcontrib>Li, Ai-jun</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computational materials science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Su, Juan-hua</au><au>Li, He-jun</au><au>Liu, Ping</au><au>Dong, Qi-ming</au><au>Li, Ai-jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Aging process optimization for a copper alloy considering hardness and electrical conductivity</atitle><jtitle>Computational materials science</jtitle><date>2007-02-01</date><risdate>2007</risdate><volume>38</volume><issue>4</issue><spage>697</spage><epage>701</epage><pages>697-701</pages><issn>0927-0256</issn><eissn>1879-0801</eissn><abstract>A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.commatsci.2006.04.013</doi><tpages>5</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0927-0256 |
ispartof | Computational materials science, 2007-02, Vol.38 (4), p.697-701 |
issn | 0927-0256 1879-0801 |
language | eng |
recordid | cdi_proquest_miscellaneous_29269888 |
source | Elsevier ScienceDirect Journals |
subjects | Aging parameter optimization Cross-disciplinary physics: materials science rheology Cu–Cr–Zr–Mg alloy Electrical conductivity Exact sciences and technology Hardness Materials science Physics Solid solution, precipitation, and dispersion hardening aging Treatment of materials and its effects on microstructure and properties |
title | Aging process optimization for a copper alloy considering hardness and electrical conductivity |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T05%3A28%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Aging%20process%20optimization%20for%20a%20copper%20alloy%20considering%20hardness%20and%20electrical%20conductivity&rft.jtitle=Computational%20materials%20science&rft.au=Su,%20Juan-hua&rft.date=2007-02-01&rft.volume=38&rft.issue=4&rft.spage=697&rft.epage=701&rft.pages=697-701&rft.issn=0927-0256&rft.eissn=1879-0801&rft_id=info:doi/10.1016/j.commatsci.2006.04.013&rft_dat=%3Cproquest_cross%3E29269888%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=29269888&rft_id=info:pmid/&rft_els_id=S0927025606001108&rfr_iscdi=true |