Prediction of Jominy hardness profiles of steels using artificial neural networks
Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a f...
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| Veröffentlicht in: | Journal of Materials Engineering and Performance 1996-02, Vol.5 (1), p.57-63 |
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| container_end_page | 63 |
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| container_issue | 1 |
| container_start_page | 57 |
| container_title | Journal of Materials Engineering and Performance |
| container_volume | 5 |
| creator | VERMEULEN, W. G VAN DER WOLK, P. J DE WEIJER, A. P VAN DER ZWAAG, S |
| description | Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least-squares model using the same set of data. |
| doi_str_mv | 10.1007/BF02647270 |
| format | Article |
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G ; VAN DER WOLK, P. J ; DE WEIJER, A. P ; VAN DER ZWAAG, S</creator><creatorcontrib>VERMEULEN, W. G ; VAN DER WOLK, P. J ; DE WEIJER, A. P ; VAN DER ZWAAG, S</creatorcontrib><description>Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least-squares model using the same set of data.</description><identifier>ISSN: 1059-9495</identifier><identifier>EISSN: 1544-1024</identifier><identifier>DOI: 10.1007/BF02647270</identifier><identifier>CODEN: JMEPEG</identifier><language>eng</language><publisher>New York, NY: Springer</publisher><subject>Applied sciences ; ARTIFICIAL INTELLIGENCE ; AUSTENITE ; BORON ; CHEMICAL COMPOSITION ; Exact sciences and technology ; HARDNESS ; HEAT TREATMENTS ; LOW ALLOY STEELS ; MATERIALS SCIENCE ; Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology ; Metals. 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A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least-squares model using the same set of data.</description><subject>Applied sciences</subject><subject>ARTIFICIAL INTELLIGENCE</subject><subject>AUSTENITE</subject><subject>BORON</subject><subject>CHEMICAL COMPOSITION</subject><subject>Exact sciences and technology</subject><subject>HARDNESS</subject><subject>HEAT TREATMENTS</subject><subject>LOW ALLOY STEELS</subject><subject>MATERIALS SCIENCE</subject><subject>Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology</subject><subject>Metals. Metallurgy</subject><subject>MICROSTRUCTURE</subject><subject>PHASE TRANSFORMATIONS</subject><subject>PREDICTION EQUATIONS</subject><subject>QUENCHING</subject><subject>STATISTICAL MODELS</subject><issn>1059-9495</issn><issn>1544-1024</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpd0N1LHDEQAPClVKgffelfsBbxQVjN12Yvj3qorRzYQvscsrOTGruXaCZLuf_e1ROFPs3A_GaYmar6wtkpZ6w7u7hiQqtOdOxDtctbpRrOhPo456w1jVGm_VTtEd2zGQuhdqufPzIOAUpIsU6-vknrEDf1nctDRKL6IScfRqTnGhXEkeqJQvxTu1yCDxDcWEec8kso_1L-SwfVjncj4efXuF_9vrr8tfzWrG6vvy_PVw1IJUoDyniH0i8GBcwprjk64LobvAbW99r1g0bhZetMj5pBq4dB9FJ2ba86o0DuV4fbuYlKsAShINxBihGhWME4M2Y2x1sz3_E4IRW7DgQ4ji5imsgKLTmTXM7w63_wPk05zvtbIYTsODeLGZ1sEeRElNHbhxzWLm8sZ_b5_fb9_TM-ep3oCNzos4sQ6K1DmEXbLbh8ArnEhDw</recordid><startdate>19960201</startdate><enddate>19960201</enddate><creator>VERMEULEN, W. 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J ; DE WEIJER, A. P ; VAN DER ZWAAG, S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c342t-c49fae3f8d4c0a4161eac167df6c0bb6abd6e2f35a9be60c56dd2b3375b4794c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Applied sciences</topic><topic>ARTIFICIAL INTELLIGENCE</topic><topic>AUSTENITE</topic><topic>BORON</topic><topic>CHEMICAL COMPOSITION</topic><topic>Exact sciences and technology</topic><topic>HARDNESS</topic><topic>HEAT TREATMENTS</topic><topic>LOW ALLOY STEELS</topic><topic>MATERIALS SCIENCE</topic><topic>Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology</topic><topic>Metals. Metallurgy</topic><topic>MICROSTRUCTURE</topic><topic>PHASE TRANSFORMATIONS</topic><topic>PREDICTION EQUATIONS</topic><topic>QUENCHING</topic><topic>STATISTICAL MODELS</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>VERMEULEN, W. G</creatorcontrib><creatorcontrib>VAN DER WOLK, P. J</creatorcontrib><creatorcontrib>DE WEIJER, A. 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G</au><au>VAN DER WOLK, P. J</au><au>DE WEIJER, A. P</au><au>VAN DER ZWAAG, S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Jominy hardness profiles of steels using artificial neural networks</atitle><jtitle>Journal of Materials Engineering and Performance</jtitle><date>1996-02-01</date><risdate>1996</risdate><volume>5</volume><issue>1</issue><spage>57</spage><epage>63</epage><pages>57-63</pages><issn>1059-9495</issn><eissn>1544-1024</eissn><coden>JMEPEG</coden><abstract>Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least-squares model using the same set of data.</abstract><cop>New York, NY</cop><pub>Springer</pub><doi>10.1007/BF02647270</doi><tpages>7</tpages></addata></record> |
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| subjects | Applied sciences ARTIFICIAL INTELLIGENCE AUSTENITE BORON CHEMICAL COMPOSITION Exact sciences and technology HARDNESS HEAT TREATMENTS LOW ALLOY STEELS MATERIALS SCIENCE Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology Metals. Metallurgy MICROSTRUCTURE PHASE TRANSFORMATIONS PREDICTION EQUATIONS QUENCHING STATISTICAL MODELS |
| title | Prediction of Jominy hardness profiles of steels using artificial neural networks |
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