Neural network classification of surface quality after hard turning of 105WCr6 steel
The paper presents the results of a surface quality study after hard turning on a CNC lathe. Ring workpieces made of 105WCr6 steel and hardened to HRC 55 are used in this work. Data was obtained on surface quality and type of chips in a three-factor experiment for end face cutting. In order to asses...
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description | The paper presents the results of a surface quality study after hard turning on a CNC lathe. Ring workpieces made of 105WCr6 steel and hardened to HRC 55 are used in this work. Data was obtained on surface quality and type of chips in a three-factor experiment for end face cutting. In order to assess the surface quality, it was photographed on an optical microscope with 4, 10, 40 times magnification. The surface quality was evaluated by traces of processing and divided into three types: the absence of moire, a clear moire, and an intermediate type of surface. The chip morphology was divided into the following categories: discontinuous, snarled and ribbon chips. To predict both parameters for different cutting conditions artificial neural networks (ANNs) were used. Different ANNs are applied to achieve the best classification results. In this work probabilistic neural network (PNN), feedforward network and learning vector quantization (LVQ) network are used. The results of modeling all networks are similar and can be used for technological preparation of production. |
doi_str_mv | 10.1088/1757-899X/537/3/032056 |
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The results of modeling all networks are similar and can be used for technological preparation of production.</description><subject>Artificial neural networks</subject><subject>Chips</subject><subject>Classification</subject><subject>Morphology</subject><subject>Neural networks</subject><subject>Optical microscopes</subject><subject>Quality assessment</subject><subject>Rapid prototyping</subject><subject>Surface properties</subject><subject>Turning (machining)</subject><subject>Vector quantization</subject><subject>Workpieces</subject><issn>1757-8981</issn><issn>1757-899X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkE9LxDAQxYMouK5-BQl48bJ20jRJe5Rl_QOrHlzRW0jTRLvWtpukyH57WyorguDpDcx7b4YfQqcELgikaUQEE7M0y14iRkVEI6AxML6HJrvF_m5OySE68n4NwEWSwASt7k3nVIVrEz4b9451pbwvbalVKJsaNxb7zlmlDd50qirDFisbjMNvyhU4dK4u69fBRYA9zx3HPhhTHaMDqypvTr51ip6uFqv5zWz5cH07v1zOdAIizGJQPLbGWpKlhinIYyh6paC5KIAyTfMEklgpYomgJhWZKQrFrSrynDNN6BSdjb2tazad8UGum_6l_qSMGe_hQMZo7-KjS7vGe2esbF35odxWEpADQTnAkQMo2ROUVI4E-2A8Bsum_Wn-N3T-R-jucfHLJtvC0i-2JYCz</recordid><startdate>20190501</startdate><enddate>20190501</enddate><creator>Rastorguev, D A</creator><creator>Sevastyanov, A A</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20190501</creationdate><title>Neural network classification of surface quality after hard turning of 105WCr6 steel</title><author>Rastorguev, D A ; Sevastyanov, A A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-20a62feff198e5a0b20de5a30c67d035c3b4042aa1f173e879edda6fadbb65c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Chips</topic><topic>Classification</topic><topic>Morphology</topic><topic>Neural networks</topic><topic>Optical microscopes</topic><topic>Quality assessment</topic><topic>Rapid prototyping</topic><topic>Surface properties</topic><topic>Turning (machining)</topic><topic>Vector quantization</topic><topic>Workpieces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rastorguev, D A</creatorcontrib><creatorcontrib>Sevastyanov, A A</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>IOP conference series. 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subjects | Artificial neural networks Chips Classification Morphology Neural networks Optical microscopes Quality assessment Rapid prototyping Surface properties Turning (machining) Vector quantization Workpieces |
title | Neural network classification of surface quality after hard turning of 105WCr6 steel |
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