ArticleMaterials (Basel, Switzerland)2022
Prediction of Selected Mechanical Properties of Polymer Composites with Alumina Modifiers.
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 7 papers.
What it found
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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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Who cites it
7 citing papers in PubMed, 33 citations in OpenAlex.
- Machine Learning-Based Prediction of Polymer Properties Using Structure-Property Relationship Modeling.Polymers · 2026Article
- Influence of Citrus Fruit Waste Filler on the Physical Properties of Silicone-Based Composites.Materials (Basel, Switzerland) · 2023Article
- Article
- Analysis of the Effectiveness of Removing Surface Defects by Brushing.Materials (Basel, Switzerland) · 2022Article
- Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art.International journal of molecular sciences · 2022Review
- Computationally Efficient Concept of Representative Directions for Anisotropic Fibrous Materials.Polymers · 2022Article
- Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions.Polymers · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Forecasting is one of the cognitive methods based on empirical knowledge supported by appropriate modeling methods that give information about the way the relations between factors and how the phenomenon under study will develop in the future. In this article, a selection is made of a suitable architecture for a predictive model for a set of data obtained during testing of the properties of polymer composites with a matrix in the form of epoxy resin with trade name L285 (Havel Composites) with H285 MGS hardener (Havel Composites), and with the addition of the physical modifier noble alumina with mass percentages of 5%, 10%, 15%, 20% and 25% for the following grain sizes: F220, F240, F280, F320, F360, respectively. In order to select the optimal architecture for the predictive model, the results of the study were tested on five types of predictive model architectures results were tested on five types of prediction model architectures, with five-fold validation, including the mean square error (MSE) metric and R2 determined for Young's modulus (E
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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 OpenQuestion graph.