Surface Roughness Modeling and Prediction by ANN when Drilling Udimet 720
Article deals with design of artificial neural network (ANN) for prediction of the surface roughness as one of the important indicators of machined surface quality. Back propagation neural network was trained and tested for prediction of the machined surface roughness. Cutting conditions, selected m...
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| Veröffentlicht in: | Key engineering materials 2014-01, Vol.581, p.366-371 |
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| container_title | Key engineering materials |
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| creator | Vrabel, Marek Mankova, Ildiko Beno, Jozef |
| description | Article deals with design of artificial neural network (ANN) for prediction of the surface roughness as one of the important indicators of machined surface quality. Back propagation neural network was trained and tested for prediction of the machined surface roughness. Cutting conditions, selected monitoring indices and tool wear parameter were given as inputs to the ANN. Test sample was nickel based super alloy Udimet 720, which is used as material for highly stressed jet engine components. Experimental data collected from tests were used as input into ANN to identify the sensitivity among cutting conditions, monitoring indices and progressive tool wear and machined surface roughness. |
| doi_str_mv | 10.4028/www.scientific.net/KEM.581.366 |
| format | Article |
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Back propagation neural network was trained and tested for prediction of the machined surface roughness. Cutting conditions, selected monitoring indices and tool wear parameter were given as inputs to the ANN. Test sample was nickel based super alloy Udimet 720, which is used as material for highly stressed jet engine components. Experimental data collected from tests were used as input into ANN to identify the sensitivity among cutting conditions, monitoring indices and progressive tool wear and machined surface roughness.</description><subject>Cutting tool materials</subject><subject>Cutting wear</subject><subject>Learning theory</subject><subject>Materials selection</subject><subject>Mathematical models</subject><subject>Monitoring</subject><subject>Neural networks</subject><subject>Nickel base alloys</subject><subject>Superalloys</subject><subject>Surface roughness</subject><issn>1013-9826</issn><issn>1662-9795</issn><issn>1662-9795</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqN0M1LwzAYx_EiCs6X_yEn8dKalzVNLqLofEE3Rd05ZMlTl9GlmrSU_fdGJ3j1lBx-fOH5ZNkJwcUYU3E2DEMRjQPfudqZwkN39jCZFqUgBeN8JxsRzmkuK1nupj8mLJeC8v3sIMYVxowIUo6y-9c-1NoAemn796WHGNG0tdA4_460t-g5gHWmc61Hiw26nM3QsASProNrfjZz69bQoYrio2yv1k2E49_3MJvfTN6u7vLHp9v7q8vH3DBOulxKTSwVWmturVjQsiKCyTGVXGpTWwME8AJkzVlNQFR2IVlpaMmYBSnG44odZqfb7kdoP3uInVq7aKBptIe2j4rwipQVZZil6fl2akIbY4BafQS31mGjCFbfhioZqj9DlQxVMlTJUCXDFLjYBrqgfezALNWq7YNP9_038QUVe4KH</recordid><startdate>20140101</startdate><enddate>20140101</enddate><creator>Vrabel, Marek</creator><creator>Mankova, Ildiko</creator><creator>Beno, Jozef</creator><general>Trans Tech Publications Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20140101</creationdate><title>Surface Roughness Modeling and Prediction by ANN when Drilling Udimet 720</title><author>Vrabel, Marek ; Mankova, Ildiko ; Beno, Jozef</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-99a1d28aaa6dd8b257183942969acfdce1e0be9f63f1e87db935c2533de984473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Cutting tool materials</topic><topic>Cutting wear</topic><topic>Learning theory</topic><topic>Materials selection</topic><topic>Mathematical models</topic><topic>Monitoring</topic><topic>Neural networks</topic><topic>Nickel base alloys</topic><topic>Superalloys</topic><topic>Surface roughness</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vrabel, Marek</creatorcontrib><creatorcontrib>Mankova, Ildiko</creatorcontrib><creatorcontrib>Beno, Jozef</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Key engineering materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vrabel, Marek</au><au>Mankova, Ildiko</au><au>Beno, Jozef</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Surface Roughness Modeling and Prediction by ANN when Drilling Udimet 720</atitle><jtitle>Key engineering materials</jtitle><date>2014-01-01</date><risdate>2014</risdate><volume>581</volume><spage>366</spage><epage>371</epage><pages>366-371</pages><issn>1013-9826</issn><issn>1662-9795</issn><eissn>1662-9795</eissn><abstract>Article deals with design of artificial neural network (ANN) for prediction of the surface roughness as one of the important indicators of machined surface quality. Back propagation neural network was trained and tested for prediction of the machined surface roughness. Cutting conditions, selected monitoring indices and tool wear parameter were given as inputs to the ANN. Test sample was nickel based super alloy Udimet 720, which is used as material for highly stressed jet engine components. Experimental data collected from tests were used as input into ANN to identify the sensitivity among cutting conditions, monitoring indices and progressive tool wear and machined surface roughness.</abstract><pub>Trans Tech Publications Ltd</pub><doi>10.4028/www.scientific.net/KEM.581.366</doi><tpages>6</tpages></addata></record> |
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| ispartof | Key engineering materials, 2014-01, Vol.581, p.366-371 |
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| source | Scientific.net Journals |
| subjects | Cutting tool materials Cutting wear Learning theory Materials selection Mathematical models Monitoring Neural networks Nickel base alloys Superalloys Surface roughness |
| title | Surface Roughness Modeling and Prediction by ANN when Drilling Udimet 720 |
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