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Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization.

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Title: Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization.
Authors: Erdoğan, Beytullah1 (AUTHOR) beytullah.erdogan@beun.edu.tr, Kılıç, İrfan2 (AUTHOR), Güneş, Abdulsamed3 (AUTHOR), Yaman, Orhan4 (AUTHOR), Çakır Şencan, Ayşegül1 (AUTHOR)
Source: Nanomaterials (2079-4991). Jul2025, Vol. 15 Issue 13, p1008. 22p.
Subjects: Dynamic viscosity, Nanofluids, Cost analysis, Viscosity, Cooling, Gaussian processes, Nanoparticles, Metalworking lubricants
Abstract: 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), TiO2, and Al2O3 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 (R2 = 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. [ABSTRACT FROM AUTHOR]
Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization.
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  Data: <searchLink fieldCode="JN" term="%22Nanomaterials+%282079-4991%29%22">Nanomaterials (2079-4991)</searchLink>. Jul2025, Vol. 15 Issue 13, p1008. 22p.
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  Label: Abstract
  Group: Ab
  Data: 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), TiO2, and Al2O3 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 (R2 = 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/nano15131008
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 1008
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      – SubjectFull: Dynamic viscosity
        Type: general
      – SubjectFull: Nanofluids
        Type: general
      – SubjectFull: Cost analysis
        Type: general
      – SubjectFull: Viscosity
        Type: general
      – SubjectFull: Cooling
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      – SubjectFull: Gaussian processes
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      – SubjectFull: Nanoparticles
        Type: general
      – SubjectFull: Metalworking lubricants
        Type: general
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      – TitleFull: Enhancing Cutting Oil Efficiency with Nanoparticle Additives: A Gaussian Process Regression Approach to Viscosity and Cost Optimization.
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            NameFull: Erdoğan, Beytullah
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            NameFull: Kılıç, İrfan
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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