Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm
Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur 302017, India.
Review Article
Global Journal of Engineering and Technology Advances, 2024, 20(03), 042–052.
Article DOI: 10.30574/gjeta.2024.20.3.0167
Publication history:
Received on 25 July 2024; revised on 03 September 2024; accepted on 06 September 2024
Abstract:
It is possible to produce improved material performance through the utilization of computational intelligence approaches such as genetic algorithms optimization technique which are discussed in this paper. The optimization of processes and the development of models that are driven by data are the key uses of these technologies. This article offers a comprehensive introduction of the topic of materials and discusses the ways in which computational intelligence techniques might be utilized to develop new materials. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of the Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ61 graphene nanoplate (GNP) composite.
Keywords:
Materials design; Optimization; Genetic algorithm
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