Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN)
Machining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effec...
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MDPI AG
2025-04-01
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| author | Beytullah Erdoğan Abdulsamed Güneş İrfan Kılıç Orhan Yaman |
| author_facet | Beytullah Erdoğan Abdulsamed Güneş İrfan Kılıç Orhan Yaman |
| author_sort | Beytullah Erdoğan |
| collection | DOAJ |
| description | Machining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effect of thermal conductivity, an important property influenced by the densities of mono and hybrid nanofluids. To this end, various nanofluids were prepared by incorporating hexagonal boron nitride (hBN), zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO<sub>2</sub>), and aluminum oxide (Al<sub>2</sub>O<sub>3</sub>) nanoparticles into sunflower oil as the base fluid. Hybrid nanofluids were created by combining two nanoparticles, including ZnO + MWCNT, hBN + MWCNT, hBN + ZnO, hBN + TiO<sub>2</sub>, hBN + Al<sub>2</sub>O<sub>3</sub>, and TiO<sub>2</sub> + Al<sub>2</sub>O<sub>3</sub>. A dataset consisting of 180 data points was generated by measuring the thermal conductivity and density of the prepared nanofluids at various temperatures (30–70 °C) in a laboratory setting. Conducting thermal conductivity measurements across different temperature ranges presents significant challenges, requiring considerable time and resources, and often resulting in high costs and potential inaccuracies. To address these issues, a feedforward artificial neural network (FFANN) method was proposed to predict thermal conductivity. Our multilayer FFANN model takes as input the temperature of the experimental environment where the measurement is made, the measured thermal conductivity of the relevant nanoparticle, and the relative density of the nanoparticle. The FFANN model predicts the thermal conductivity value linearly as output. The model demonstrated high predictive accuracy, with a reliability of R = 0.99628 and a coefficient of determination (R<sup>2</sup>) of 0.9999. The average mean absolute error (MAE) for all hybrid nanofluids was 0.001, and the mean squared error (MSE) was 1.76 × 10<sup>−6</sup>. The proposed FFANN model provides a State-of-the-Art approach for predicting thermal conductivity, offering valuable insights into selecting optimal hybrid nanofluids based on thermal conductivity values and nanoparticle density. |
| format | Article |
| id | doaj-art-e3d94cd50abb4a66a1f5047a8e48818a |
| institution | OA Journals |
| issn | 2072-666X |
| language | English |
| publishDate | 2025-04-01 |
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| series | Micromachines |
| spelling | doaj-art-e3d94cd50abb4a66a1f5047a8e48818a2025-08-20T02:33:58ZengMDPI AGMicromachines2072-666X2025-04-0116550410.3390/mi16050504Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN)Beytullah Erdoğan0Abdulsamed Güneş1İrfan Kılıç2Orhan Yaman3Department of Mechanical Engineering, Zonguldak Bülent Ecevit University, Zonguldak 67100, TurkeyDepartment of Electric and Energy, Firat University, Elazığ 23119, TurkeyDepartment of Information Technology, Firat University, Elazığ 23119, TurkeyDepartment of Forensic Engineering, Firat University, Elazığ 23119, TurkeyMachining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effect of thermal conductivity, an important property influenced by the densities of mono and hybrid nanofluids. To this end, various nanofluids were prepared by incorporating hexagonal boron nitride (hBN), zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO<sub>2</sub>), and aluminum oxide (Al<sub>2</sub>O<sub>3</sub>) nanoparticles into sunflower oil as the base fluid. Hybrid nanofluids were created by combining two nanoparticles, including ZnO + MWCNT, hBN + MWCNT, hBN + ZnO, hBN + TiO<sub>2</sub>, hBN + Al<sub>2</sub>O<sub>3</sub>, and TiO<sub>2</sub> + Al<sub>2</sub>O<sub>3</sub>. A dataset consisting of 180 data points was generated by measuring the thermal conductivity and density of the prepared nanofluids at various temperatures (30–70 °C) in a laboratory setting. Conducting thermal conductivity measurements across different temperature ranges presents significant challenges, requiring considerable time and resources, and often resulting in high costs and potential inaccuracies. To address these issues, a feedforward artificial neural network (FFANN) method was proposed to predict thermal conductivity. Our multilayer FFANN model takes as input the temperature of the experimental environment where the measurement is made, the measured thermal conductivity of the relevant nanoparticle, and the relative density of the nanoparticle. The FFANN model predicts the thermal conductivity value linearly as output. The model demonstrated high predictive accuracy, with a reliability of R = 0.99628 and a coefficient of determination (R<sup>2</sup>) of 0.9999. The average mean absolute error (MAE) for all hybrid nanofluids was 0.001, and the mean squared error (MSE) was 1.76 × 10<sup>−6</sup>. The proposed FFANN model provides a State-of-the-Art approach for predicting thermal conductivity, offering valuable insights into selecting optimal hybrid nanofluids based on thermal conductivity values and nanoparticle density.https://www.mdpi.com/2072-666X/16/5/504thermal conductivitynanoparticlefeedforward artificial neural network (FFANN)cutting fluids |
| spellingShingle | Beytullah Erdoğan Abdulsamed Güneş İrfan Kılıç Orhan Yaman Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) Micromachines thermal conductivity nanoparticle feedforward artificial neural network (FFANN) cutting fluids |
| title | Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) |
| title_full | Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) |
| title_fullStr | Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) |
| title_full_unstemmed | Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) |
| title_short | Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) |
| title_sort | predicting thermal conductivity of nanoparticle doped cutting fluid oils using feedforward artificial neural networks ffann |
| topic | thermal conductivity nanoparticle feedforward artificial neural network (FFANN) cutting fluids |
| url | https://www.mdpi.com/2072-666X/16/5/504 |
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