ArticlePolymers2024
A Multi-Objective Optimization of Neural Networks for Predicting the Physical Properties of Textile Polymer Composite Materials.
Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
- Review
- Article
- Multi-Objective Optimization and Prediction of Mechanical Properties of Green Basalt Fiber-Reinforced Concrete Using Evolutionary ML Algorithms.Materials (Basel, Switzerland) · 2026Article
- Coefficient of Linear Thermal Expansion of Polymers and Polymer Composites: A Comprehensive Review.Polymers · 2025Review
- Review
- Scientific Machine Learning for Polymeric Materials.Polymers · 2025Article
- A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel.Materials (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
This paper explores the application of multi-objective optimization techniques, including MOPSO, NSGA II, and SPEA2, to optimize the hyperparameters of artificial neural networks (ANNs) and support vector machines (SVMs) for predicting the physical properties of textile polymer composite materials (TPCMs). The optimization process utilizes data on the physical characteristics of the constituent fibers and fabrics used to manufacture these composites. By employing optimization algorithms, we aim to enhance the predictive accuracy of the ANN and SVM models, thereby facilitating the design and development of high-performance textile polymer composites. The effectiveness of the proposed approach is demonstrated through comparative analyses and validation experiments, highlighting its potential for optimizing complex material systems.
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Registered trials
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