ArticleScientific reports2026
Intelligent prediction and optimization of nanofluid-assisted turning using hybrid generative deep learning.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Nanofluid-assisted machining is a promising sustainable alternative to the conventional cutting fluids; however, the correct prediction and optimization of the latter process is still difficult due to nonlinearities inherent to it and the lack of experimental data. Hence, the current study presents an innovative hybrid generative deep-learning model that synergistically integrates Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Generative Adversarial Network (GAN) to predict intelligently and optimize multi-fold in the nanofluid-assisted turning of EN31 steel. It has been experimentally shown that MWCNT based gravity fed nanofluid decreases the surface roughness by 12.83% and cutting force components by up to 23.4% when compared to traditional coolant. The RSM model has a moderate predictive power (R
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