Computational intelligence based design of age-hardenable aluminium alloys for different temperature regimes Swati Dey a, ⁎, Nashrin Sultana a , Md Salim Kaiser b , Partha Dey c , Shubhabrata Datta d a Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India b Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh c Academy of Technology, Hooghly 712121, India d Bankura Unnayani Institute of Engineering, Bankura 722146, India abstract article info Article history: Received 29 June 2015 Received in revised form 12 December 2015 Accepted 14 December 2015 Available online 17 December 2015 Computational intelligence based approaches are used in tandem to design novel age-hardenable aluminium alloy, which would utilize the effect of all precipitate forming elements together, crossing the limit of the compo- sitions defined within different series. A pool of data is created from the tensile properties of age-hardenable al- uminium alloys in the 2XXX, 6XXX and 7XXX series. Based on the testing temperature the data is segregated, and different models for the tensile properties in the different temperature regimes are developed using Artificial Neural Network (ANN). The inherent relation between the composition and processing variables with the me- chanical properties are explored using sensitivity analysis (SA). In order to design alloys with the conflicting ob- jectives of high strength and adequate ductility, Multi-Objective Genetic Algorithm (MOGA) is used to search optimum solutions using the ANN models as the objective functions. The Pareto solutions from MOGA and the SA results are used along with prior knowledge of the alloy systems to design age-hardenable aluminium alloys with improved mechanical properties at different temperature regimes. The designed composition, which is be- yond any of the age-hardenable series, has been developed experimentally, with encouraging results and inter- esting observations. © 2015 Elsevier Ltd. All rights reserved. Keywords: Age-hardenable aluminium alloy Tensile properties Artificial Neural Network Multi-objective optimization Genetic algorithm Alloy design 1. Introduction Aluminium alloys that respond to heat treatment are the age- hardenable or precipitation hardened alloys. At elevated temperature, the second phase dissolves in the solid solution, which precipitates upon quenching and ageing at a lower temperature. For an aluminium alloy to be age hardened the second phase must be soluble at elevated temperatures, but must show decreasing solubility with decreasing temperature. This second condition of decreasing solubility with tem- perature puts a limit on the number of useful precipitation-hardened alloy systems [1]. With proper alloying and heat treatment, hardness in precipitation hardened alloy can be increased to nearly 40 times as compared to pure aluminium alloys. Therefore, it is one of the most im- portant strengthening mechanisms in aluminium [2]. The common age hardenable alloys are the Al–Cu (2XXX), Al–Mg–Si (6XXX) and Al–Zn– Mg (7XXX) series of alloys, which have specific stable and metastable precipitates and their fixed precipitation sequences for each system. To improve the properties of these alloys, several attempts have been made for years through minor additions [3–5] or through thermal [6–8] or thermomechanical processing [9–14]. The performance of an alloy might be improved only if the bound- aries of the series of alloys could be crossed and if the effects of the pre- cipitates of the different series could be incorporated. But such effort has never been reported by any previous worker. In this work, attempts have been made to design alloys with improved mechanical properties having such composition, which can have precipitates of all the three age hardenable alloy series. Experimental trial-and-error method to search for suitable chemical composition and processing parameters that will lead to the desired material properties is tedious, time consum- ing and costly, with no warranted results. On the other hand, unveiling the mathematical interrelationships between the composition and pro- cessing parameters with the materials properties of the alloy will make it possible to computationally design an alloy possessing the optimal combination of strength and ductility [15]. The complex correlation being difficult to describe through any physical model, data-driven models have been used extensively in the materials engineering domain [16–18] to successfully find such complex correlations leading to effective materials design. Applications of this computational approach to the design of Al alloys and composites have also been reported [19–25]. While computational intelligence techniques, particularly using rough and fuzzy set theories have seen wide application in map- ping the complex composition–processing–property relationships [26–28], Artificial Neural Network (ANN) [29,30] seems to be the most widely used paradigm in this domain to satisfactorily extract non- Materials and Design 92 (2016) 522–534 ⁎ Corresponding author. E-mail address: swatidey@yahoo.com (S. Dey). http://dx.doi.org/10.1016/j.matdes.2015.12.076 0264-1275/© 2015 Elsevier Ltd. All rights reserved. Contents lists available at ScienceDirect Materials and Design journal homepage: www.elsevier.com/locate/matdes