Internal Friction in an Epoxy Polymer and a Fiberglass Reinforced Plastic Based on It

Kalinin, Yu. E.; Кудрин, А М; Ovdak, O. V.; Popov, I. I.

doi:10.1134/s0965545x22010047

Cited by 4 publications

(1 citation statement)

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“…This material exhibits a high strength-to-weight ratio [ 77 ], excellent corrosion resistance [ 78 , 79 ], and dimensional stability [ 80 ], making it suitable for applications requiring durability and structural integrity [ 81 ]. We considered types such as T-10 [ 82 ], T-13 [ 83 ], T-11 [ 84 ], T-SU 8/3(VMP)-78 [ 85 ], and T-25 [ 86 ]. The fabric construction of these samples predominantly consisted of canvas [ 87 ] and satin weaves [ 88 ].…”

Section: Methodsmentioning

confidence: 99%

A Multi-Objective Optimization of Neural Networks for Predicting the Physical Properties of Textile Polymer Composite Materials

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Polymers

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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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