Optimization with artificial intelligence of the machinability of Hardox steel, which is exposed to different processes

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dc.authoridSöyler, Hasan/0000-0003-1717-1212
dc.authorwosidSöyler, Hasan/AFC-2140-2022
dc.contributor.authorAltug, Mehmet
dc.contributor.authorSoyler, Hasan
dc.date.accessioned2024-08-04T20:54:38Z
dc.date.available2024-08-04T20:54:38Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, different process types were processed on Hardox 400 steel. These processes were carried out with five different samples as heat treatment, cold forging, plasma welding, mig-mag welding and commercial sample. The aim here is to determine the changes in properties such as microstructure, microhardness and conductivity that occur in the structure of hardox 400 steel when exposed to different processes. Then, the samples affected by these changes were processed in WEDM with the box-behnken experimental design. Ra, Kerf, MRR and WWR results were analyzed in Minitab 21 program. In the continuation of the study, using these data, a prediction models were created for Ra, Kerf, MRR and WWR with Deep Learning (DL) and Extreme Learning Machine (ELM). Anaconda program Python 3.9 version was used as a program in the optimization study. In addition, a linear regression models are presented to comparison the results. According to the results the lowest Ra values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best Ra (surface roughness) value of 1.92 mu m was obtained in the heat treated sample and in the experiment with a time off of 250 mu s. Model F value in ANOVA analysis for Ra is 86.04. Model for Ra r2 value was obtained as 0.9534. The lowest kerf values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best kerf value of 200 mu was obtained in the heat treated sample and in the experiment with a time off of 200 mu s. Model F value in ANOVA analysis for Kerf is 90.21. Model for Kerf r2 value was obtained as 0.9555. Contrary to Ra and Kerf, it is desirable to have high MRR values. On average, the highest MRR values were obtained in mig-mag welded, plasma welded, cold forged, master sample and heat-treated processes, respectively. The best mrr value of 200 gmin- 1 was obtained in the mig-mag welded sample and in the experiment with a time off of 300 mu s. Model for MRR r2 value was obtained as 0.9563. The lowest WWR values were obtained in heat-treated, cold forged, master sample, plasma welded and mig- mag welded processes, respectively. The best wwr value of 0.098 g was obtained in the heat treated sample and in the experiment with a time off of 200 mu s. Model F value in ANOVA analysis for WWR is 92.12. Model for wwr r2 value was obtained as 0.09561. In the analysis made with artificial intelligence systems; The best test MSE value for Ra was obtained as 0.012 in DL and the r squared value 0.9274. The best test MSE value for kerf was obtained as 248.28 in ELM and r squared value 0.8676. The best MSE value for MRR was obtained as 0.000101 in DL and the r squared value 0.9444. The best MSE value for WWR was obtained as 0.000037 in DL and the r squared value 0.9184. As a result, it was concluded that different optimization methods can be applied according to different outputs (Ra, Kerf, MRR, WWR). It also shows that artificial intelligencebased optimization methods give successful estimation results about Ra, Kerf, MRR, WWR values. According to these results, ideal DL and ELM models have been presented for future studies.en_US
dc.description.sponsorshipInonu University [FBA-2018-610]en_US
dc.description.sponsorshipThis study is supported by Inonu University Scientific Researches Projects with number FBA-2018-610. We thank the Rectorate of Inonu University for their support. After the great earthquakes experienced on 6 th of February 2023 in southeast of Turkiye, which turned our lives upside down; We would like to thank and express our gratitude to the Dean of the Faculty of Engineering at 19 Mayis University for opening its doors to us and providing an academic working environment and opportunities.en_US
dc.identifier.doi10.1038/s41598-023-40710-8
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid37644053en_US
dc.identifier.scopus2-s2.0-85168970329en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41598-023-40710-8
dc.identifier.urihttps://hdl.handle.net/11616/101546
dc.identifier.volume13en_US
dc.identifier.wosWOS:001058035700016en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherNature Portfolioen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMaterial Removal Rateen_US
dc.subjectSurface-Roughnessen_US
dc.subjectPredictionen_US
dc.subjectParametersen_US
dc.subjectWearen_US
dc.subjectToolen_US
dc.titleOptimization with artificial intelligence of the machinability of Hardox steel, which is exposed to different processesen_US
dc.typeArticleen_US

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