Optimization of aluminum oxynitride compaction process using a Gray-coded genetic algorithm

Chakraborti, Nirupam; Mishra, Pallavi; Banerjee, Anupam

doi:10.1016/s0167-577x(03)00431-2

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…There have been several works for molecular structure calculation for several clusters. [219][220][221][222][223][224][225][226] These works were extensively covered by Chakraborti in an earlier publication. 8 Computer modelling calculations were used to design new molecules of desired performance by inserting gold nanoparticles and aromatic fragments in the oligo-(dibenzyl sulphide) molecules using either full search or GA. 227 Similar studies are also reported elsewhere.…”

Section: Other Applicationsmentioning

confidence: 99%

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Mitra¹

2008

International Materials Reviews

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Genetic algorithms (GAs) belong to the broader family of generic population based metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the biology inspired computing techniques that emulate the basic Darwinian concept of natural selection. Because of their many positive attributes such as ease of use, versatility and robustness, GAs have become important in materials research very rapidly since their inception. Though going through a major stage of theoretical development, GAs have been successfully applied in many fields such as polymer production, polymer design, polymer processing, polymer laminates and other polymers, as well as non-polymer applications in recent times. The present review attempts to present the state of the art in this area. The review is broadly divided into three sections: fundamentals of GAs, the use of GAs in polymeric materials and the use of GAs in non-polymer applications. First, the fundamentals of GAs are presented to provide some glimpse of the technique; this is followed by presentations on applications of GAs in various polymeric fields, such as in polymer production, polymer scheduling, polymer design, polymer laminates and other polymer related applications and non-polymeric fields, e.g. metallic laminates and other metallic material applications. A thorough literature review and critical analysis of the review is provided and attempts have been made to portray how evolutionary optimisation techniques, GAs in particular, are used across a variety of applications.

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Section: Other Applicationsmentioning

confidence: 99%

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Mitra¹

2008

International Materials Reviews

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“…GDE is a multi-objective version of the original Differential Evolution (DE) algorithm [12]. It deals with a real coded population, instead of a binary or a Gray representation [13], and devises its own crossover and mutation in the real space. Differential Evolution uses the concept of fitness in the same sense as in Simple Genetic Algorithms (SGA) [14].…”

Section: The Computing Strategy Used In the Second Modelmentioning

confidence: 99%

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

Chakraborti

Kumar

Babu

et al. 2006

Computational Materials Science

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Several variants of Genetic Algorithms have been used to study the surface profiles of hot rolled slabs, quantified in terms of crown, a major parameter related to their geometric tolerances. Two different models are presented, and simulations in a multi-objective mode are carried out to generate the relevant Pareto fronts, which, in turn, are tested against the operational data of an integrated steel plant.

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“…Evolutionary multi-objective optimisation of powder compaction and sintering has been subjected to a number of rigorous studies in recent years. 72,[159][160][161][162][163] These studies successfully integrated an existing micromechanical model 164,165 and the limited experimental data in the general framework of multi-objective, as well as single objective, GAs. Useful information was thus generated for a number of powder systems, including copper, tantalum, and beryllium, which has also been extended to include a special utility material, namely, aluminium oxynitride (ALON).…”

Section: Studies On Metallic Materialsmentioning

confidence: 99%

Genetic algorithms in materials design and processing

Chakraborti¹

2004

International Materials Reviews

195

102

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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.

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Optimization of aluminum oxynitride compaction process using a Gray-coded genetic algorithm

Cited by 9 publications

References 16 publications

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Genetic algorithms in polymeric material production, design, processing and other applications: a review

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

Genetic algorithms in materials design and processing

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