Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms
In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the proce...
Gespeichert in:
| Veröffentlicht in: | Precision engineering 1998-10, Vol.22 (4), p.243-251 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 251 |
|---|---|
| container_issue | 4 |
| container_start_page | 243 |
| container_title | Precision engineering |
| container_volume | 22 |
| creator | Ko, Tae Jo Kim, Hee Sool |
| description | In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs. |
| doi_str_mv | 10.1016/S0141-6359(98)00019-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_744704304</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0141635998000191</els_id><sourcerecordid>744704304</sourcerecordid><originalsourceid>FETCH-LOGICAL-c368t-2e70454f92ef382d2237d4a2803f2e608fe32e920c93dd63626d4a3738a2a1f83</originalsourceid><addsrcrecordid>eNqFkEtLxDAQx4MouK5-BKEHQT1U8-im6Ulk8QULHnxcQ0gmNdI2a5Iu-O3NPtCjMDAw85v5z_wROiX4imDCr18wqUjJ2ay5aMQlxpg0JdlDEyJqVlJW0300-UUO0VGMnxmqBa4m6P12TH7wvR9joceU3NAWSxVUDwlCEaAdO5WcH4oxrlvKqGVyKyh6b6DbVAZTtDBAcrpQXeuDSx99PEYHVnURTnZ5it7u717nj-Xi-eFpfrsoNeMilRRqXM0q21CwTFBD87WmUlRgZilwLCwwCg3FumHGcMYpz21WM6GoIlawKTrf7l0G_zVCTLJ3UUPXqQHyS7KuqqzAckzRbEvq4GMMYOUyuF6Fb0mwXNsoNzbKtUeyEXJjoyR57mynoKJWnQ1q0C7-DXMq6gpn7GaLQf525SDIqB0MGowLoJM03v0j9AP0Soc8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>744704304</pqid></control><display><type>article</type><title>Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Ko, Tae Jo ; Kim, Hee Sool</creator><creatorcontrib>Ko, Tae Jo ; Kim, Hee Sool</creatorcontrib><description>In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.</description><identifier>ISSN: 0141-6359</identifier><identifier>EISSN: 1873-2372</identifier><identifier>DOI: 10.1016/S0141-6359(98)00019-1</identifier><identifier>CODEN: PREGDL</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Adaptive control systems ; adaptive modeling ; Applied sciences ; Artificial intelligence ; Backpropagation ; Computer science; control theory; systems ; Connectionism. Neural networks ; Exact sciences and technology ; Genetic algorithms ; Iterative methods ; Mechanical engineering. Machine design ; neural network ; Neural networks ; optimization ; Turning ; turning process</subject><ispartof>Precision engineering, 1998-10, Vol.22 (4), p.243-251</ispartof><rights>1998 Elsevier Science Inc.</rights><rights>1999 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-2e70454f92ef382d2237d4a2803f2e608fe32e920c93dd63626d4a3738a2a1f83</citedby><cites>FETCH-LOGICAL-c368t-2e70454f92ef382d2237d4a2803f2e608fe32e920c93dd63626d4a3738a2a1f83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S0141-6359(98)00019-1$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,46000</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1628740$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ko, Tae Jo</creatorcontrib><creatorcontrib>Kim, Hee Sool</creatorcontrib><title>Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms</title><title>Precision engineering</title><description>In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.</description><subject>Adaptive control systems</subject><subject>adaptive modeling</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Backpropagation</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Genetic algorithms</subject><subject>Iterative methods</subject><subject>Mechanical engineering. Machine design</subject><subject>neural network</subject><subject>Neural networks</subject><subject>optimization</subject><subject>Turning</subject><subject>turning process</subject><issn>0141-6359</issn><issn>1873-2372</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><recordid>eNqFkEtLxDAQx4MouK5-BKEHQT1U8-im6Ulk8QULHnxcQ0gmNdI2a5Iu-O3NPtCjMDAw85v5z_wROiX4imDCr18wqUjJ2ay5aMQlxpg0JdlDEyJqVlJW0300-UUO0VGMnxmqBa4m6P12TH7wvR9joceU3NAWSxVUDwlCEaAdO5WcH4oxrlvKqGVyKyh6b6DbVAZTtDBAcrpQXeuDSx99PEYHVnURTnZ5it7u717nj-Xi-eFpfrsoNeMilRRqXM0q21CwTFBD87WmUlRgZilwLCwwCg3FumHGcMYpz21WM6GoIlawKTrf7l0G_zVCTLJ3UUPXqQHyS7KuqqzAckzRbEvq4GMMYOUyuF6Fb0mwXNsoNzbKtUeyEXJjoyR57mynoKJWnQ1q0C7-DXMq6gpn7GaLQf525SDIqB0MGowLoJM03v0j9AP0Soc8</recordid><startdate>19981001</startdate><enddate>19981001</enddate><creator>Ko, Tae Jo</creator><creator>Kim, Hee Sool</creator><general>Elsevier Inc</general><general>Elsevier Science</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TC</scope></search><sort><creationdate>19981001</creationdate><title>Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms</title><author>Ko, Tae Jo ; Kim, Hee Sool</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-2e70454f92ef382d2237d4a2803f2e608fe32e920c93dd63626d4a3738a2a1f83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Adaptive control systems</topic><topic>adaptive modeling</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Backpropagation</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Genetic algorithms</topic><topic>Iterative methods</topic><topic>Mechanical engineering. Machine design</topic><topic>neural network</topic><topic>Neural networks</topic><topic>optimization</topic><topic>Turning</topic><topic>turning process</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ko, Tae Jo</creatorcontrib><creatorcontrib>Kim, Hee Sool</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Mechanical Engineering Abstracts</collection><jtitle>Precision engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ko, Tae Jo</au><au>Kim, Hee Sool</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms</atitle><jtitle>Precision engineering</jtitle><date>1998-10-01</date><risdate>1998</risdate><volume>22</volume><issue>4</issue><spage>243</spage><epage>251</epage><pages>243-251</pages><issn>0141-6359</issn><eissn>1873-2372</eissn><coden>PREGDL</coden><abstract>In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><doi>10.1016/S0141-6359(98)00019-1</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0141-6359 |
| ispartof | Precision engineering, 1998-10, Vol.22 (4), p.243-251 |
| issn | 0141-6359 1873-2372 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_744704304 |
| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Adaptive control systems adaptive modeling Applied sciences Artificial intelligence Backpropagation Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Genetic algorithms Iterative methods Mechanical engineering. Machine design neural network Neural networks optimization Turning turning process |
| title | Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T09%3A14%3A36IST&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=Autonomous%20cutting%20parameter%20regulation%20using%20adaptive%20modeling%20and%20genetic%20algorithms&rft.jtitle=Precision%20engineering&rft.au=Ko,%20Tae%20Jo&rft.date=1998-10-01&rft.volume=22&rft.issue=4&rft.spage=243&rft.epage=251&rft.pages=243-251&rft.issn=0141-6359&rft.eissn=1873-2372&rft.coden=PREGDL&rft_id=info:doi/10.1016/S0141-6359(98)00019-1&rft_dat=%3Cproquest_cross%3E744704304%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=744704304&rft_id=info:pmid/&rft_els_id=S0141635998000191&rfr_iscdi=true |