Determination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural network

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dc.authoridOZTURK, Savas/0000-0003-2661-4556
dc.authoridKayabasi, Erhan/0000-0002-3603-6211
dc.contributor.authorKayabasi, Erhan
dc.contributor.authorOzturk, Savas
dc.contributor.authorCelik, Erdal
dc.contributor.authorKurt, Huseyin
dc.date.accessioned2024-09-29T16:00:46Z
dc.date.available2024-09-29T16:00:46Z
dc.date.issued2017
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAn Artificial Neural Network (ANN) simulation was utilized to predict surface roughness values (R-a) for a Silicon (Si) ingot cutting operation with a Diamond Wire Saw (DWS) cutting machine. Experiments were done on a DWS cutting machine to obtain data for training, testing and validation of the ANN. The DWS cutting operation had three parameters affecting surface quality: spool speed, z axis speed and oil ratio in a coolant slurry. Other parameters such as wire tension, wire thickness, and work piece diameter were assumed as constant. The DWS cutting machine performed 28 cutting operations with different values of the selected three parameters and new cutting parameters were derived for different cutting conditions to achieve the best surface quality by using the ANN. Wafers 400 mu m thick were cut from a n-type single crystalline Si ingot in a STX 1202 DWS cutting machine. R-a values were measured three times from different regions of the wafers. In ANN simulation 70% of R-a values were used as training, 15% of R-a values were used as validation and 15% of R-a values were used to test data in ANN. The ANN simulation results validated training output data with success above 99%. Consequently, the R-a values corresponding to the cutting parameters, and also proper cutting parameters for specific R-a values were determined for DWS cutting using the ANN. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipScientific Research Projects Coordination Unit of Karabuk University, Turkey [KBU-BAP-14/1-DR-003]; Electronic Materials Production and Application Center (EMUM) at Dokuz Eylul University, Turkeyen_US
dc.description.sponsorshipThis experimental research was supported by the Scientific Research Projects Coordination Unit of Karabuk University (Project Number KBU-BAP-14/1-DR-003), Turkey and Electronic Materials Production and Application Center (EMUM) at Dokuz Eylul University, Turkey.en_US
dc.identifier.doi10.1016/j.solener.2017.04.022
dc.identifier.endpage293en_US
dc.identifier.issn0038-092X
dc.identifier.scopus2-s2.0-85017562302en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage285en_US
dc.identifier.urihttps://doi.org/10.1016/j.solener.2017.04.022
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5324
dc.identifier.volume149en_US
dc.identifier.wosWOS:000401688800024en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofSolar Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSi waferen_US
dc.subjectArtificial neural networken_US
dc.subjectCutting parametersen_US
dc.subjectSurface roughnessen_US
dc.titleDetermination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural networken_US
dc.typeArticleen_US

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