ArticlePolymers2025
Machine Learning-Based Prediction of Tribological Properties of Epoxy Composite Coating.
Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications.Polymers · 2025Review
- Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites.Scientific reports · 2025Article
- Epoxy Adhesive Materials as Protective Coatings: Strength Property Analysis Using Machine Learning Algorithms.Materials (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Machine learning, being convenient and nondestructive, is beneficial for evaluating the tribological properties of coatings. Here, six machine learning algorithms, using a sericite/epoxy composite coating (SEC) as an example, were employed to assess the impact of filler content (10, 15, 20, 25, and 30 wt%) and mesh size on the tribological properties of epoxy composite coatings under different loads. The results showed that the gradient boosting regression model had superior accuracy and stability compared to the other regression models, achieving friction coefficient and wear rate prediction accuracies of 93.7% and 85.7%, respectively. This model outperformed others, including decision trees, extreme gradient boosting, and Gaussian process regression. Feature importance showed that the content of sericite had the most significant influence on the tribological properties. This work provides valuable guidance for the engineering application of this material.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.