Sol-gel and co-precipitation synthesized hybrid nanofluids for enhanced CNC turning of AISI 4340 steel: an experimental and machine learning approach.
| Title: | Sol-gel and co-precipitation synthesized hybrid nanofluids for enhanced CNC turning of AISI 4340 steel: an experimental and machine learning approach. |
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| Authors: | Efa DA; School of Mechanical Engineering, Institute of Technology, Wallaga University, P.O. Box.395, Nekemte, Ethiopia. dameeifaa@wollegauniversity.edu.et.; Ifa DA; School of Mechanical Engineering, Institute of Technology, Wallaga University, P.O. Box.395, Nekemte, Ethiopia.; Dejene ND; School of Mechanical Engineering, Institute of Technology, Wallaga University, P.O. Box.395, Nekemte, Ethiopia.; Belachew HZ; Department of Chemistry, College of Natural and Computational Science, Dambi Dollo University, Dambi Dollo, Ethiopia.; Department of Chemistry, College of Natural and Computational Science, Jimma University, Jimma, Ethiopia.; Tegegn DF; Department of Chemistry, College of Natural and Computational Science, Dambi Dollo University, Dambi Dollo, Ethiopia. |
| Source: | Scientific reports [Sci Rep] 2025 Nov 21; Vol. 15 (1), pp. 41207. Date of Electronic Publication: 2025 Nov 21. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Abstract: | Machining high-strength alloys, such as AISI 4340 steel, presents significant challenges in terms of surface integrity, production efficiency, and heat dissipation. This study investigated the effects of a novel hybrid nanofluid of copper oxide (CuO) and aluminum oxide (Al2O3) nanoparticles to improve CNC turning of AISI 4340 steel. The experiments were conducted under a range of cutting conditions by varying the cutting speed, depth of cut and feed rate, along with the concentration of the hybrid nanofluid. A new methodology for preparing and applying the hybrid nanofluid demonstrated sufficient cooling and lubrication properties, enabling machining tests that improved upon traditional methods. The experimental study indicated that as the cutting speed and feed rate increased, the cutting temperature and surface roughness also increased significantly. Increasing the nanofluid concentration (0.25-0.45%) lowered the tool tip temperature and surface roughness due to increased thermal conductivity and formation of a protective tribological film. However, beyond 0.45% hybrid nanofluid concentration, the performance declined due to increased fluid viscosity and agglomeration of nanoparticles. An Artificial Neural Network (ANN) demonstrated significant predictive accuracy, with coefficients of determination (R2) of 0.864 for tool tip temperature, 0.828 for surface roughness, and 0.942 for material removal rate (MRR). The Genetic Algorithm (GA) determined the optimal nanofluid concentration of 0.4%, cutting speed of 80 m/min, feed rate of 0.07 mm/rev, and depth of cut of 0.4 mm. Experimental data confirmed ANN predictions with an error range of less than ± 2%, and confirmatory trials demonstrated that heat was dissipated, showing improved surface quality and MRR.; (© 2025. The Author(s).) |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
| References: | Nanomaterials (Basel). 2022 Nov 27;12(23):. (PMID: 36500836); Food Chem. 2025 Jun 30;478:143569. (PMID: 40037223); Heliyon. 2024 Dec 05;10(24):e40969. (PMID: 39735623); Polymers (Basel). 2024 Oct 18;16(20):. (PMID: 39458759); Nat Commun. 2024 Apr 6;15(1):2984. (PMID: 38582903) |
| Contributed Indexing: | Keywords: AISI 4340 steel; Advanced machine learning; CNC turning; Nanoparticles |
| Entry Date(s): | Date Created: 20251121 Latest Revision: 20251124 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12638806 |
| DOI: | 10.1038/s41598-025-25102-4 |
| PMID: | 41271859 |
| Database: | MEDLINE |
Journal Article