Genetic algorithms in materials design and processing

Abstract

Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection. They are highly robust and efficient for most engineering optimising studies. Although a late entrant in the materials arena, GAs based studies are increasingly making their presence felt in many different aspects of this discipline. In recent times, GAs have been successfully used in numerous problems in the areas of atomistic material design, alloy design, polymer processing, powder compaction and sintering, ferrous production metallurgy, continuous casting, metal rolling, metal cutting, welding, and so on. The present review attempts to present the state of the art in this area. It includes three broad sections given as: fundamentals of genetic algorithms, genetic algorithms in materials design, and genetic algorithms in materials processing. The first section provides the reader with the basic concepts and the intricacies associated with this novel technique. The following section presents a detailed account of the usage of GAs to design various materials, predominantly at the atomic level. The third section aims to capture the process of applicability of GAs in numerous materials processing operations. A thorough literature search and critical analysis of the research conducted so far is provided, and attempts have been made to demonstrate how one single methodology can be utilised to study virtually every area of the vast materials discipline.

Imprecise knowledge based design and development of titanium alloys for prosthetic applications

“…For costs a weighted summation model of the elemental constituent is developed with the recent cost of the alloying additions. In case of multi-objective optimization, unlike the single objective optimization, a set of non-dominated solutions, where each solution is better than other in at least one objective, evolve;this set of solutions is called Pareto solutions [38][39][40]. In the present study, the compositions of alloys selected from the evolved Pareto solutions are developed for experimental trials.…”

Optimizing surface profiles during hot rolling: A genetic algorithms based multi-objective optimization

“…Only a limited amount of Genetic Algorithms related studies have been conducted for the rolling process and in this context one really has a very limited amount of prior work to fall back upon [6]. Here we have attempted to solve the problem in two different forms as discussed below.…”

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

“…In addition, these algorithms are robust and are easily portable for one type of problem to another with no significant loss of accuracy. Traditionally, these types of algorithms have been widely used for solving the single objective optimisation problems in the materials domain, [15][16][17][18][19] and now their usage in the multi-objective problems is quite prominent. 20 Some of the significant materials related applications are now described.…”

“…Recently the genetic and evolutionary algorithms 2,3 based approaches have made some very significant contributions in this area. [15][16][17][18][19] These algorithms are non-traditional, in that they are of non-calculus type, and do not use gradient or derivative information, but are nature inspired, thus tend to mimic biological processes like crossover and mutation as well as the group behaviour of a flock of birds, a colony of ants, or the working principles of the immune systems in the developed species. In addition, these algorithms are robust and are easily portable for one type of problem to another with no significant loss of accuracy.…”