ArticleMaterials (Basel, Switzerland)2022
Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Physics-Informed Machine Learning for Carbonation Depth Prediction in Concrete.Materials (Basel, Switzerland) · 2026Article
- Cheminformatics Approaches to the Analysis of Additives for Sustainable Polymeric Materials.Polymers · 2025Article
- A Novel Concept of Nano-Enhanced Phase Change Material.Materials (Basel, Switzerland) · 2024Article
- Fractal Characterization of the Mass Loss of Bronze by Erosion-Corrosion in Seawater.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cavitation is a physical process that produces different negative effects on the components working in conditions where it acts. One is the materials' mass loss by corrosion-erosion when it is introduced into fluids under cavitation. This research aims at modeling the mass variation of three samples (copper, brass, and bronze) in a cavitation field produced by ultrasound in water, using four artificial intelligence methods-SVR, GRNN, GEP, and RBF networks. Utilizing six goodness-of-fit indicators (R
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Registered trials
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