Study on methods to improve the mechanical properties of aramid/epoxy composites

Yermakhanova, A.M.; Baiserikov, Berdiyar Meirzhanuly; Kenzhegulov, Aidar; Meiirbekov, Mohammed Nurgazyuly; Zhumadilov, Bauyrzhan Yerbolsynuly

doi:10.1177/00952443221147645

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…The prepregs used in the study have been obtained by impregnating a one-directional carbon woven reinforcing filler 12K-300-230 with an epoxy binder with the following composition: 100 weight parts modified epoxy-diane resin; 20 weight parts aromatic amine hardener 4,4'-diamino diphenyl sulfone, 10 weight parts plasticizer tricresyl phosphate [21][22][23][24].…”

Section: Methodsmentioning

confidence: 99%

Study of Prepregs Lifetime Based on Epoxy Resin With Aromatic Amine Hardener

Baiserikov

2023

Eurasian phys. tech. j.

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Today, there is a steady trend in the use of carbon fiber-reinforced plastics in the aerospace and defense industries, mainly based on using pre-impregnated semi-finished products - prepregs. Producing carbon fiber parts from prepregs often requires a long manufacturing time, during which the prepreg must maintain its performance properties. Therefore, one of the main characteristics of prepreg is its viability. This work developed a suitable resin composition with optimized properties and a methodology for studying the viability of prepreg samples, based on determining the stickiness and degree of curing of the prepreg depending on storage time. The influence of prepreg storage time on the strength characteristics of cured laminates was studied. Prepreg samples with long-term viability of up to 60 days were obtained. The experimental data obtained are of practical significance in the industrial production of carbon fiber products, as they establish a connection between the performance properties of the prepreg and storage time.

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Section: Methodsmentioning

confidence: 99%

Study of Prepregs Lifetime Based on Epoxy Resin With Aromatic Amine Hardener

Baiserikov

2023

Eurasian phys. tech. j.

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Deep‐Learning‐Based Prediction of Long‐Term Piezoresistive Sensing Performance of MXene/Aramid Nanofiber Sensors

Chen,

Qin,

Gong

et al. 2024

Adv Eng Mater

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Flexible compressible sensors are widely used in the human health monitoring field for their numerous advantages. However, the dynamic loads and possible injuries associated with long‐term living and exercise pose a challenge to the long‐term piezoresistive performance stability of these sensors. In this study, the application of deep learning for predicting the long‐term performance of these sensors is explored, aiming to enhance the assessment of sensor stability and ensure accurate and reliable long‐term monitoring. Samples with different Ti3C2Tx MXene/aramid nanofiber mass ratios (1:1, 1:2, 1:3) are prepared and piezoresistive characterization is conducted under long‐term loading cycles to obtain training data. Three distinct deep‐learning prediction models, convolutional neural network (CNN), long short‐term memory, and recurrent neural network (RNN), are utilized to assess their influence on prediction accuracy. To assess the effectiveness of the proposed method, its prediction of long‐term piezoresistive sensing performance with experimental data not used for training purposes is compared. The CNN model demonstrates optimal results with a mean absolute error of 0.0251 for the 1:3 mass ratio sample. Based on the experimental results, the model is expected to be integrated into human health monitoring systems, thus improving the assessment of sensor stability throughout its lifetime.

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