
Journal of Engineering Research, Год журнала: 2024, Номер unknown
Опубликована: Окт. 1, 2024
Язык: Английский
Journal of Engineering Research, Год журнала: 2024, Номер unknown
Опубликована: Окт. 1, 2024
Язык: Английский
Ain Shams Engineering Journal, Год журнала: 2024, Номер 16(1), С. 103206 - 103206
Опубликована: Дек. 7, 2024
Язык: Английский
Процитировано
10Biochemistry and Biophysics Reports, Год журнала: 2025, Номер 41, С. 101912 - 101912
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2International Journal of Hydrogen Energy, Год журнала: 2025, Номер 114, С. 337 - 351
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
1AIP Advances, Год журнала: 2025, Номер 15(4)
Опубликована: Апрель 1, 2025
This study introduces a novel approach for predicting the mechanical properties of 3D-printed polylactic acid wood composites using gene expression programming (GEP) and artificial neural networks (ANN) modeling methods. Addressing challenge determining optimal process parameters in fused deposition natural fiber composites, experiments were designed Taguchi’s L27 orthogonal array. Five key analyzed: layer thickness (100–300 μm), printing speed (40–90 mm/s), raster angle (0°–90°), infill density (35%–95%), nozzle temperature (200–220 °C). ANOVA results identified as most influential factor, contributing 38.36% 26% to tensile compressive strengths, respectively. Subsequently, comparative statistical analysis evaluated predictive accuracy GEP ANN. The model exhibited superior performance, achieving validation errors between 0.04% 0.82%, outperforming ANN (0.34%–5.31%). These findings provide robust framework enhancing performance sustainable enabling more efficient reliable production processes additive manufacturing.
Язык: Английский
Процитировано
1Alexandria Engineering Journal, Год журнала: 2025, Номер 118, С. 325 - 336
Опубликована: Янв. 23, 2025
Язык: Английский
Процитировано
0Journal of Materials Research and Technology, Год журнала: 2025, Номер 35, С. 2736 - 2754
Опубликована: Янв. 31, 2025
Язык: Английский
Процитировано
0Alexandria Engineering Journal, Год журнала: 2025, Номер 121, С. 38 - 52
Опубликована: Фев. 25, 2025
Язык: Английский
Процитировано
0Results in Engineering, Год журнала: 2025, Номер unknown, С. 104467 - 104467
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Water Air & Soil Pollution, Год журнала: 2025, Номер 236(4)
Опубликована: Март 11, 2025
Язык: Английский
Процитировано
0AIP Advances, Год журнала: 2025, Номер 15(3)
Опубликована: Март 1, 2025
In recent years, sustainability has evolved profoundly and garnered significant global attention, establishing itself as a pivotal topic in contemporary research. line with this development, the present review thoroughly examines existing studies on machining processes employing minimum quantity lubrication (MQL). The growing imperative for sustainable practices driven researchers to reassess alternative techniques within operations. Although conventional lubri-cooling agents continue be widely used engineering alloys, an expanding body of research demonstrates that incorporation vegetable oils, nanofluids, nanoplatelets into MQL systems can yield superior performance compared traditional methods. presents overview developments advancements related technology provides rigorous analysis oils nanofluids metalworking fluids. This study also eco-friendly approaches flood serves meaningful resource move toward greener solutions.
Язык: Английский
Процитировано
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