Load-Bearing Capacity of Elements of Composite Shell Structures in Rocket and Space Engineering Made of Composite Materials

Kravchuk, L.; Buiskikh, K. P.; Derevyanko, I. I.; Potapov, O. M.

doi:10.1007/s11223-022-00439-7

Cited by 5 publications

(1 citation statement)

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“…This material offers exceptional strength, stiffness, and lightweight properties, making it ideal for aerospace [ 90 ], automotive [ 91 ], and sporting goods [ 92 ] applications where weight reduction and high performance are critical. We considered CC245 [ 93 ], CC206 [ 94 ], T700SC [ 95 ], UMT49 [ 96 ], UT-900-3 [ 97 ], HTS45 [ 98 ], and IMS65 [ 99 ]. The weave pattern observed in these samples primarily included twill [ 100 ] and unidirectional [ 101 ] weaves.…”

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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