Alternative proposal, based on systems-engineering methods, aimed at substituting with carbon-epoxy laminates the load-bearing aluminum sandwiches employed in the structure of a small satellite

Rojas, Erik Vargas; Nocetti-Cotelo, Carlos-Alberto

doi:10.1016/j.asr.2020.04.004

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Launch vehicles and aerospace vehicles have put forward higher requirements for the specific strength and specific modulus of their materials. Honeycomb sandwich composites (HSCs) are made of two high-strength facings and a lightweight honeycomb core (i.e., aluminum, titanium as well as paper), and they have been widely used in the primary and secondary load-bearing structures of aerospace vehicles due to their high specific stiffness, high specific strength, low density, good thermal insulation, as well as vibration reduction [1][2][3]. During the preparation process or long-term work in a harsh environment, HSCs may generate defects or damages (i.e., debonding, collapse, skin delamination, ponding, impact damage, etc.…”

Section: Introductionmentioning

confidence: 99%

Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)

Wang¹,

Gao

Wang

et al. 2023

Metals

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This report is on convolution neural network (CNN) fusion lock-in thermography, which can implement the intelligent identification of defects for aviation honeycomb sandwich composites (HSCs). First, HSCs specimens with subsurface delamination defects were fabricated and stimulated by halogen lamps according to sinusoidal modulation, and the defects were reliably inspected using lock-in thermography. The amplitude and phase images (commonly referred to as feature images) were obtained by using a digital lock-in correlation algorithm. Furthermore, these feature images were changed into gray or color-level image formalism datasets, which is pre-processed in ways including contrast enhancement, threshold segmentation as well as mosaic data augmentation. Finally, the four-layer feature pyramid structure and ransformer are combined and introduced to the popular YOLOv5 CNN model, and a YOLOLT CNN model is formed to realize the defect identification. The average precision (AP) in the defect identification of HSCs in complex environments (contains noise and other objects) reached 93.2% and achieved an average recognition speed of 0.6 s/image.

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Section: Introductionmentioning

confidence: 99%

Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)

Wang¹,

Gao

Wang

et al. 2023

Metals

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“…With the maximum specific strength and stiffness, a carbon fibre honeycomb core features the minimum coefficient of linear thermal expansion [13,14]. Combined with face sheets made of carbon fibre composites, the carbon fibre honeycomb core allows producing chemically homogeneous and dimensionally stable structures that are widely used in the aerospace industry [15,16]. A carbon fibre honeycomb core is a new structural material for aerospace engineering.…”

Section: Introductionmentioning

confidence: 99%

Effect of Ply Orientation on the Mechanical Performance of Carbon Fibre Honeycomb Cores

Kondratiev,

Píštěk,

Gajdachuk

et al. 2023

Polymers

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Carbon fibres used as a honeycomb core material (subject to a proper in-depth analysis of their reinforcement patterns) allows solving the thermo-dimensional stability problem of the units for space systems. Based on the results of numerical simulations with the support of finite element analysis, the paper provides an evaluation of the accuracy of analytical dependencies for the determination of the moduli of elasticity of a carbon fibre honeycomb core in tension/compression and shear. It is shown that a carbon fibre honeycomb reinforcement pattern has a significant impact on the mechanical performance of the carbon fibre honeycomb core. For example, for honeycombs measuring 10 mm in height, the maximum shear modulus values corresponding to the reinforcement pattern of ±45° exceed the minimum values for a reinforcement pattern of 0° and 90° by more than 5 times in the XOZ plane and 4 times for the shear modulus in the YOZ plane. The maximum modulus of the elasticity of the honeycomb core in the transverse tension, corresponding to a reinforcement pattern of ±75°, exceeds the minimum modulus for the reinforcement pattern of ±15° more than 3 times. We observe a decrease in the values of the mechanical performance of the carbon fibre honeycomb core depending on its height. With a honeycomb reinforcement pattern of ±45°, the decrease in the shear modulus is 10% in the XOZ plane and 15% in the YOZ plane. The reduction in the modulus of elasticity in the transverse tension for the reinforcement pattern does not exceed 5%. It is shown that in order to ensure high-level moduli of elasticity with respect to tension/compression and shear at the same time, it is necessary to focus on a reinforcement pattern of ±64°. The paper covers the development of the experimental prototype technology that produces carbon fibre honeycomb cores and structures for aerospace applications. It is shown by experiments that the use of a larger number of thin layers of unidirectional carbon fibres provides more than a 2-time reduction in honeycomb density while maintaining high values of strength and stiffness. Our findings can permit a significant expansion of the area of application relative to this class of honeycomb cores in aerospace engineering.

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Analysis of Gluing Assembly Defects of Ultra-Thin Composite Face Sheets with Honeycomb Core

Gaidachuk

Nabokina

Taranenko

et al. 2023

Lecture Notes in Networks and Systems

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Alternative proposal, based on systems-engineering methods, aimed at substituting with carbon-epoxy laminates the load-bearing aluminum sandwiches employed in the structure of a small satellite

Cited by 5 publications

References 31 publications

Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)

Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)

Effect of Ply Orientation on the Mechanical Performance of Carbon Fibre Honeycomb Cores

Analysis of Gluing Assembly Defects of Ultra-Thin Composite Face Sheets with Honeycomb Core

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