Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and Related Fields: Part II

Paszkowicz, W.

doi:10.1080/10426914.2012.746707

Cited by 84 publications

(34 citation statements)

References 172 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ever-increasing presence of the genetic and evolutionary algorithms in the materials domain, as elaborated in some recent overviews [44,51], suggests that this constitutes a step in the right direction. Despite their enormous practical importance, reported GAs applications in this area are few and far between despite some renewed interest in the recent times [7][8][9]52], although computational studies on the superalloys are being quite vigorously pursued [53].…”

Section: Methodsmentioning

confidence: 99%

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Jha

Pettersson

Dulikravich

et al. 2014

Materials and Manufacturing Processes

View full text Add to dashboard Cite

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.

show abstract

Section: Methodsmentioning

confidence: 99%

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Jha

Pettersson

Dulikravich

et al. 2014

Materials and Manufacturing Processes

View full text Add to dashboard Cite

show abstract

“…The mating process entails combining pairs of parents (alloys) into offspring by means of a process called crossover 57,58 . This process can be illustrated as follows.…”

Section: B Sqs Generationmentioning

confidence: 99%

Calculations of planar defect energies in substitutional alloys using the special-quasirandom-structure approach

Jong

Olmsted

et al. 2016

Phys. Rev. B

View full text Add to dashboard Cite

A method is described for calculating the energetics of planar defects in alloys based on the special-quasirandom-structure (SQS) approach. We examine the accuracy of the approach employing atomistic calculations based on a classical embedded-atom-method (EAM) interatomic potential for hexagonal close packed (hcp) alloys, for which benchmark results can be obtained by direct configurational averaging. The results of these calculations demonstrate that the SQS-based approach can be employed to derive the concentration dependence of the energies of twin boundaries, unstable stacking faults and surfaces to within an accuracy of approximately 10 %. The SQS considered in this study contain up to 72 atoms and hence are small enough to be considered in first-principles density-functional-theory (DFT) based calculations. The application of the SQS-based approach in direct DFT-based calculations is demonstrated in a study of the concentration dependence of interfacial energies for {1121} twins in hcp Ti-Al alloys.

show abstract

“…Among the studies of evolutionary computation related to engineering problems, Paszkowicz [22] indicated that genetic algorithms (GAs) assist in solving many engineering optimization problems. That study indicated the most recent (years 2008-2012) applications of GAs in materials science and related fields (solid state physics and chemistry, crystallography, production, and engineering), where GAs helped solve many parameter design problems.…”

Section: Lgp Testmentioning

confidence: 99%

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Wang¹

2014

Materials and Manufacturing Processes

View full text Add to dashboard Cite

The light guide plate (LGP) printing process is used to obtain the optimal optical brightness, which requires the optimization of the LGP printing process parameters. This study analyzed the optical brightness optimization parameter design. This study integrated the back-propagation neural network (BPN) and a revised genetic algorithm (GA) to identify the optimal experimental level combination of the optical brightness parameter design. The obtained optical brightness process capability C pk by the proposed method was 1.73, which was better than the process capability C pk of 1.65 obtained by using the Taguchi method.

show abstract

Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and Related Fields: Part II

Cited by 84 publications

References 172 publications

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Calculations of planar defect energies in substitutional alloys using the special-quasirandom-structure approach

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Contact Info

Product

Resources

About