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

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 optimiza...

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Bibliographic Details
Published in:Materials 2023-10, Vol.16 (20), p.6794
Main Authors: Miller, Bartosz, Ziemiański, Leonard
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Business metrics
Composite materials
Composite structures
Cost analysis
Design optimization
Efficiency
Genetic algorithms
Identification
Laminates
Load
Mathematical models
Multiple criterion
Multiple objective analysis
Network reliability
Neural networks
Optimization
Optimization techniques
Parameters
Performance evaluation
Performance measurement
Shells
Shells (structural forms)
Strength to weight ratio
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Summary: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.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma16206794