A Comprehensive Review on High-Fidelity and Metamodel-Based Optimization of Composite Laminates

Kalita, Kanak; Haldar, Salil; Chakraborty, Shankar

doi:10.1007/s11831-021-09699-z

Cited by 12 publications

(4 citation statements)

References 259 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Equation (11), we obtain the laminate mechanical properties matrix A k , also known in the literature as ABD or ABDR (with the extension of transverse shearing properties) matrix, in which:…”

Section: Numerical Homogenization Of Precast Reinforced Concrete Slabmentioning

confidence: 99%

“…This disadvantage can be eliminated by using selected mechanical and numerical methods. Building a complex model (i) can be shortened, for instance by using proven homogenization techniques [9,10] or metamodeling [11]. This will enable the mapping of the mechanical behavior of even a complex structure, similar to the paper of Staszak et al [12], in which the sandwich panels were considered for homogenization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Networks to Numerical Homogenization of the Precast Hollow-Core Concrete Slabs

Gajewski,

Skiba

2024

Applied Sciences

View full text Add to dashboard Cite

The main goal of this work is to combine the usage of the numerical homogenization technique for determining the effective properties of representative volume elements with artificial neural networks. The effective properties are defined according to the classical laminate theory. The purpose is to create and train a rapid surrogate model for the quick calculation of the mechanical properties of hollow concrete slabs. First, the homogenization algorithm was implemented, which determines membrane, bending and transverse shearing properties of a given parametrized hollow-core precast slab reinforced with steel bars. The algorithm uses the finite element mesh but does not require a formal solution of the finite element method problem. Second, the learning and training artificial intelligence framework was created and fed with a dataset obtained by optimal Latin hypercube sampling. In the study, a multilayer perceptron type of artificial neural network was used. This allows for obtaining rapid calculations of the effective properties of a particular hollow-core precast slab by using a surrogate model. In the paper, it has been proven that such a model, obtained via complex numerical calculations, gives a very accurate estimation of the properties and can be used in many practical tasks, such as optimization problems or computer-aided design decisions. Above all, the efficient setup of the artificial neural network has been sought and presented.

show abstract

Section: Numerical Homogenization Of Precast Reinforced Concrete Slabmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Numerical Homogenization of the Precast Hollow-Core Concrete Slabs

Gajewski,

Skiba

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Kalita et al [11] conducted a comprehensive review of nearly 300 research articles on high-fidelity and metamodel-based optimization of composite laminates. This review emphasizes various metamodels and succinctly presents each research article's methodologies and key outcomes, offering a valuable resource for future researchers and design engineers.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms

Miller,

Ziemiański

2023

Materials

View full text Add to dashboard Cite

Composite shells find diverse applications across industries due to their high strength-to-weight ratio and tailored properties. Optimizing parameters such as matrix-reinforcement ratio and orientation of the reinforcement is crucial for achieving the desired performance metrics. Stochastic optimization, specifically genetic algorithms, offer solutions, yet their computational intensity hinders widespread use. Surrogate models, employing neural networks, emerge as efficient alternatives by approximating objective functions and bypassing costly computations. This study investigates surrogate models in multi-objective optimization of composite shells. It incorporates deep neural networks to approximate relationships between input parameters and key metrics, enabling exploration of design possibilities. Incorporating mode shape identification enhances accuracy, especially in multi-criteria optimization. Employing network ensembles strengthens reliability by mitigating model weaknesses. Efficiency analysis assesses required computations, managing the trade-off between cost and accuracy. Considering complex input parameters and comparing against the Monte Carlo approach further demonstrates the methodology’s efficacy. This work showcases the successful integration of network ensembles employed as surrogate models and mode shape identification, enhancing multi-objective optimization in engineering applications. The approach’s efficiency in handling intricate designs and enhancing accuracy has broad implications for optimization methodologies.

show abstract

“…The fracture toughness of quasi-brittle materials, such as tough ceramics, ice and reinforced laminates, is of great importance for the description and characterisation of their applications [4]. These types of materials are particularly sought after in applications where specific gravity is an issue, such as aerospace [5], marine, offshore [6] and automotive [7]. Fracture toughness or crack resistance is one of the most important properties as it has a great influence on the damage and failure mechanisms of such composite structures.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study to Evaluate the Essential Work of Fracture to Measure the Fracture Toughness of Quasi-Brittle Material

et al. 2022

View full text Add to dashboard Cite

In the present work, three different woven composite laminates were fabricated using the hand lay-up method. The woven reinforcement fibres were carbon fibres (CFRP), glass fibres (GFRP-W) and (GFRP-R) in combination with epoxy resin. Then, the central notch specimen tensile test (CNT) was used to measure the fracture toughness and the corresponding surface release energy (GIC). Then, the data were compared with the essential work of fracture (we) values based on the stored energy of the body to obtain a new standard fracture toughness test for composite laminates using relatively simple techniques. In addition to an extended finite element model, XFEM was implemented over a central notch specimen geometry to obtain a satisfactory validation of the essential work of fracture concepts. Therefore, the average values of (GIC) were measured with CNT specimens 25.15 kJ/m2, 32.5 kJ/m2 and 20.22 kJ/m2 for CFRP, GFRP-W and GFRP-R, respectively. The data are very close as the percentage error for the surface release energy measured by the two methods was 0.83, 4.6 and 5.16 for carbon, glass and random fibre composite laminates, respectively. The data for the fracture toughness of XFEM are also very close. The percentage error is 4.6, 5.25 and 2.95 for carbon, glass and random fibre composite laminates, respectively. Therefore, the fundamental work of the fracture concept is highly recommended as a fracture toughness test for composite laminates or quasi-brittle Material.

show abstract

A Comprehensive Review on High-Fidelity and Metamodel-Based Optimization of Composite Laminates

Cited by 12 publications

References 259 publications

Application of Artificial Neural Networks to Numerical Homogenization of the Precast Hollow-Core Concrete Slabs

Application of Artificial Neural Networks to Numerical Homogenization of the Precast Hollow-Core Concrete Slabs

Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms

A Comparative Study to Evaluate the Essential Work of Fracture to Measure the Fracture Toughness of Quasi-Brittle Material

Contact Info

Product

Resources

About