Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force

Szklarek, Karol; Gajewski, Jakub

doi:10.3390/ma13173881

Cited by 11 publications

(5 citation statements)

References 46 publications

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“…Since its introduction in the 1960s, ANNs continued to provide a powerful framework for modeling nonlinear systems, and they were used in a wide variety of engineering applications, including automatic control [ 24 ], solar energy systems [ 25 ], traffic and transportation [ 26 ], image processing [ 27 ], optimization of structures [ 28 ], materials science and engineering [ 29 , 30 , 31 , 32 ], manufacturing [ 33 ], fracture mechanics, and fault detection [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. In fracture mechanics, ANNs were mostly used in applications concerned with crack propagation, fatigue life, and failure mode prediction [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

2020

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Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.

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Section: Introductionmentioning

confidence: 99%

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

2020

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“…Zhang et al (Zhang et al, 2008) predicted the strength characteristics of composites from the maximum stress of composites through ANN, further optimized the wrapped fiber density and filled fiber density of 2.5D braided composites using genetic algorithm, and successfully realized the weight reduction design of composites. Szklarek et al (Szklarek and Gajewski, 2020) used the combination of ANN and genetic algorithm to optimize the lamination stacking sequence of composite U-shaped plates and improve the critical buckling loading. Bisagni et al (Bisagni and Lanzi, 2002) trained the ANN to replace the non-linear finite element analysis for the post buckling optimization of composite (1992), Aboudi (1996) • Determining the spatial variation of the stresses in the phase with higher accuracy HFGMC RVE divided into any number of subcells…”

Section: Artificial Intelligence and Intelligent Algorithmmentioning

confidence: 99%

Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the-state-of-the-art

Chen

Zhang

et al. 2023

Front. Mater.

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As the application of lightweight fiber-reinforced composite structures reaches an unprecedented scale in industry, design technology for composite structures becomes crucial for enhancing performance, improving productivity, and reducing cost. In recent years, the rapid development of intelligent technology, such as big data, deep learning, and machine learning, has promoted the development of design technology. However, the current situation and intellectualization of the design technology is not well summarized. This paper reviews the advance in design technologies for fiber-reinforced composite structures, including prediction and optimization methods for composite properties. Then, their intellectualization development is overviewed. Finally, the development trend of intelligent design technologies and intelligent composite structures are discussed. This work can provide a reference for researchers in the related field.

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“…Current research has been limited to analyzing a small number of commercial adhesives. However, in order to fully analyze the influence of material properties and geometric features, it is necessary to use neural networks [ 17 , 18 , 19 ]. Neural networks are a component machine learning, which can boost the efficacy of monitoring tools.…”

Section: Introductionmentioning

confidence: 99%

The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test

2021

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Adhesive bonding are becoming increasingly important in civil and mechanical engineering, in the field of mobile applications such as aircraft or automotive. Adhesive joints offer many advantages such as low weight, uniform stress distribution, vibration damping properties or the possibility of joining different materials. The paper presents the results of numerical modeling and the use of neural networks in the analysis of dual adhesive single-lap joints subjected to a uniaxial tensile test. The dual adhesive joint was created through the use of adhesives with various parameters in terms of stiffness and strength. In the axis of the overlap, there was a point bonded joint characterized by greater stiffness and strength, and on the outside, there was a bonded joint limited by the edges of the overlap and characterized by lower stiffness and strength. It is an innovative solution for joining technology and the influence of such parameters as the thickness of one of the adherends, the radius of the point bonded joint and the material parameters of both adhesive layers were analyzed. The joint is characterized by a two-stage degradation process, i.e., after the damage of the rigid adhesive, the flexible adhesive ensures the integrity of the entire joint. For numerical modeling, the Finite Element Method (FEM) and cohesive elements was used, which served as input data to an Artificial Neural Network (ANN). The applied approach allowed the impact of individual parameters on the maximum force, initiation energy, and fracture energy to be studied.

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Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force

Cited by 11 publications

References 46 publications

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the-state-of-the-art

The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test

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