Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization
Nanoparticle additives are used to increase the cooling efficiency of cutting fluids in machining. In this study, changing dynamic viscosity values depending on the addition of nanoparticles to cutting oils was investigated. Mono nanofluids were prepared by adding hBN (hexagonal boron nitride), ZnO,...
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MDPI AG
2025-06-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/15/13/1008 |
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| author | Beytullah Erdoğan İrfan Kılıç Abdulsamed Güneş Orhan Yaman Ayşegül Çakır Şencan |
| author_facet | Beytullah Erdoğan İrfan Kılıç Abdulsamed Güneş Orhan Yaman Ayşegül Çakır Şencan |
| author_sort | Beytullah Erdoğan |
| collection | DOAJ |
| description | Nanoparticle additives are used to increase the cooling efficiency of cutting fluids in machining. In this study, changing dynamic viscosity values depending on the addition of nanoparticles to cutting oils was investigated. Mono nanofluids were prepared by adding hBN (hexagonal boron nitride), ZnO, MWCNT (multi-walled carbon nanotube), TiO<sub>2</sub>, and Al<sub>2</sub>O<sub>3</sub> as nanoparticles, hybrid nanofluids were prepared by using two types of nanoparticles (ZnO + MWCNT, hBN + MWCNT etc.), and ternary nanofluids were prepared by using three types of nanoparticles. GPR (Gaussian process regression) was used to estimate unmeasured dynamic viscosity values using the dynamic viscosity values measured for different temperatures. Dynamic viscosity results are a precise determination (R<sup>2</sup> = 1). An augmented dataset was obtained by adding the dynamic viscosity values estimated with high accuracy. A fitness function based on dynamic viscosity and nanoparticle unit costs was proposed for the cost analysis. With the help of the proposed fitness function, it was observed that the best performing nanoparticles were the ZnO and ZnO hybrid mixtures according to different dynamic viscosity and cost effects. The study showed that the most suitable nanofluid selection focused on performance and cost could be made without performing experiments under various operating conditions by increasing the limited experimental measurements with strong GPR estimates and using the proposed fitness function. |
| format | Article |
| id | doaj-art-26cf258d38594cfaa9299e95273b3453 |
| institution | DOAJ |
| issn | 2079-4991 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj-art-26cf258d38594cfaa9299e95273b34532025-08-20T03:17:08ZengMDPI AGNanomaterials2079-49912025-06-011513100810.3390/nano15131008Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost OptimizationBeytullah Erdoğan0İrfan Kılıç1Abdulsamed Güneş2Orhan Yaman3Ayşegül Çakır Şencan4 Department of Mechanical Engineering, Engineering Faculty, Zonguldak Bülent Ecevit University, Zonguldak 67100, TurkeyDepartment of Software Engineering, Engineering Faculty, Fırat University, Elazig 23119, TurkeyDepartment of Electrical and Energy, Elazığ Organized Industrial Zone Vocational School, Fırat University, Elazig 23119, TurkeyDepartment of Digital Forensic Engineering, Technology Faculty, Fırat University, Elazig 23119, Turkey Department of Mechanical Engineering, Engineering Faculty, Zonguldak Bülent Ecevit University, Zonguldak 67100, TurkeyNanoparticle additives are used to increase the cooling efficiency of cutting fluids in machining. In this study, changing dynamic viscosity values depending on the addition of nanoparticles to cutting oils was investigated. Mono nanofluids were prepared by adding hBN (hexagonal boron nitride), ZnO, MWCNT (multi-walled carbon nanotube), TiO<sub>2</sub>, and Al<sub>2</sub>O<sub>3</sub> as nanoparticles, hybrid nanofluids were prepared by using two types of nanoparticles (ZnO + MWCNT, hBN + MWCNT etc.), and ternary nanofluids were prepared by using three types of nanoparticles. GPR (Gaussian process regression) was used to estimate unmeasured dynamic viscosity values using the dynamic viscosity values measured for different temperatures. Dynamic viscosity results are a precise determination (R<sup>2</sup> = 1). An augmented dataset was obtained by adding the dynamic viscosity values estimated with high accuracy. A fitness function based on dynamic viscosity and nanoparticle unit costs was proposed for the cost analysis. With the help of the proposed fitness function, it was observed that the best performing nanoparticles were the ZnO and ZnO hybrid mixtures according to different dynamic viscosity and cost effects. The study showed that the most suitable nanofluid selection focused on performance and cost could be made without performing experiments under various operating conditions by increasing the limited experimental measurements with strong GPR estimates and using the proposed fitness function.https://www.mdpi.com/2079-4991/15/13/1008cutting fluidGaussian process regression (GPR)nanofluiddynamic viscosityfitness functioncost analysis |
| spellingShingle | Beytullah Erdoğan İrfan Kılıç Abdulsamed Güneş Orhan Yaman Ayşegül Çakır Şencan Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization Nanomaterials cutting fluid Gaussian process regression (GPR) nanofluid dynamic viscosity fitness function cost analysis |
| title | Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization |
| title_full | Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization |
| title_fullStr | Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization |
| title_full_unstemmed | Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization |
| title_short | Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization |
| title_sort | enhancing cutting oil efficiency with nanoparticle additives a gaussian process regression approach to viscosity and cost optimization |
| topic | cutting fluid Gaussian process regression (GPR) nanofluid dynamic viscosity fitness function cost analysis |
| url | https://www.mdpi.com/2079-4991/15/13/1008 |
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