aceReview of machine learning approaches for predicting mechanical behavior of composite materials
In recent years, machine learning (ML) has emerged as a powerful tool for predicting the mechanical behavior of composite materials, offering a faster, more cost-effective alternative to traditional testing and simulation methods. This review explores how various ML techniques, including random forests, support vector machines, artificial neural networks, and deep learning models, are used to forecast key material properties such as tensile strength, hardness, fracture toughness, and fatigue life. From a broad s…
ML models trained on experimental and simulation data can forecast composite mechanical properties faster and cheaper than traditional testing.
Adoption is limited by data scarcity, poor model interpretability, and absence of standardized validation protocols for composite predictions.
Progress is constrained by limited data, low interpretability, and lack of standardized validation for ML predictions in composites.
Evidence
- Peer-reviewedDiscover Applied Sciences2025-10-17
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Truvace Impact Record TRV-2026-0480, v1: “Review of machine learning approaches for predicting mechanical behavior of composite materials.” Truvace, 2026-07-22. /record/TRV-2026-0480 (accessed at citation time). sha256 b086303eee44510e…
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