ArticleScientific reports2025
Machine learning based optimization of titanium electropolishing using artificial neural networks and Taguchi design in eco-friendly electrolytes.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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1 citing paper in PubMed.
- Current Density Optimization for SERS Substrates Using Galvanostatically Anodized TiOMaterials (Basel, Switzerland) · 2025Article
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2 authors.
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Abstract
This research proposes an integrated optimization framework combining artificial neural networks (ANN) and Taguchi robust design for an eco-friendly deep eutectic solvent-based electrolyte. Five key process parameters-applied voltage, processing time, temperature, electrolyte composition (choline chloride to ethylene glycol ratio) and distilled water concentration-were systematically analyzed for their effects on the surface roughness of electropolished titanium. To address data scarcity and improve the generalization performance of the model, Gaussian noise with a mean of 0 and a standard deviation of 0.05 was applied to the input variables to augment the dataset. The multilayer perceptron-based ANN model effectively learned the nonlinear interactions between process parameters and achieved a high predictive accuracy with a coefficient of determination (R
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