Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data
Technical University of Košice · Sumy State University · +1 more institution
Abstract
The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches for optimizing their composition and properties. This study aimed at the application of machine learning for the prediction and optimization of the functional properties of composites based on a thermoplastic matrix with various fillers (two types of fibrous, four types of dispersed, and two types of nano-dispersed fillers). The experimental methods involved material production through powder metallurgy, further microstructural analysis, and mechanical and tribological testing. The microstructural analysis revealed distinct structural modifications and interfacial interactions…
Citation impact
- FWCI
- 29.19
- Percentile
- 100%
- References
- 91
Authors
4Topics & keywords
- Materials science
- Ultimate tensile strength
- Composite material
- Composite number
- Tribology
- Filler (materials)