In silico Design of High Strength Aluminium Alloy Using Multi-objective GA

Abstract

Abstract. Multi-objective optimization is employed using genetic algorithm, for designing novel age-hardenable aluminium alloy with improved properties. Data on the mechanical properties of age-hardenable aluminium alloys is considered together for modeling the mechanical properties using artificial neural network. The models are used as objective functions to get the optimized combination of input parameters for the objectives, viz. high strength and ductility. The significance analyses of the variables on the ANN models gave a primary insight on the role of the variables. The Pareto solutions emerged from the GA based multi-objective optimization is found suitable for effective design of aluminium alloys with tailored properties. An in depth study of the role of the variables in the non-dominated solutions clearly describes the guideline for developing an alloy with improved mechanical properties.

Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow

“…Multi-objective optimisation algorithms, particularly genetic algorithms (a type of evolutionary computing), have been extensively used for inverse design, thus addressing the complexities of exploring vast combinatorial spaces [12,[20][21][22][23][24]. For instance, Feng et al [12] combined a random forest model with the Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimise strength and ductility in Al-Mg-Si alloy.…”