Stress Corrosion Cracking Resistant Aluminum Alloys: Optimizing Concentrations of Alloying Elements and Tempering

Bhargava, Suvrat; Dulikravich, George S.; Murty, G. S.; Agarwal, Arvind; Colaço, Marcelo J.

doi:10.1080/10426914.2010.536938

Cited by 21 publications

(6 citation statements)

References 15 publications

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“…Thus the existence of steels with superior property combinations than any of the members in the original data set containing 800 entries, which would be almost impossible to come up by performing the experiments alone and it would not be obvious from this data set to begin with. The advantage and effectiveness of multi‐objective genetic algorithms based analysis are once again demonstrated for a system of very significant metallurgical interest and its effectiveness in alloy design13–15 is once again established.…”

Section: Resultsmentioning

confidence: 99%

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

Kumar

Chakrabarti

Chakraborti

2012

steel research int.

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A data base was put together for the mechanical properties of microalloyed steels, which contained about 800 entries for ultimate tensile strength (UTS), yield strength (YS), and elongation. Using an evolutionary neural network, based upon a predator–prey genetic algorithms of bi‐objective type, this information was used to construct data‐driven models for UTS, YS, and elongation. The optimum Pareto tradeoffs between these properties were obtained using a multi‐objective genetic algorithm. The results led to some hitherto unexplored steel compositions with optimum properties. Some such steels were actually cast and the experimentally observed property values were found to be well in accord with the predicted results.

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Section: Resultsmentioning

confidence: 99%

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

Kumar

Chakrabarti

Chakraborti

2012

steel research int.

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“…Each iteration in IOSO allows a decomposition of the initial approximation function into a set of simple approximation functions so that the final response function becomes a multi-level graph. Further details of this strategy are available elsewhere [7,12,[45][46][47][48].…”

Section: Methodsmentioning

confidence: 99%

“…This experimental dataset [12] was also earlier used [7] to determine chemical compositions of Pareto-optimal new generation of Ni-based superalloys. This work used a stochastic optimization approach known as IOSO [45][46][47][48]. IOSO is a semi-stochastic, multi-objective optimization algorithm incorporating certain aspects of a selective search on a continuously updated multidimensional response surface.…”

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

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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.

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“…Regression techniques are often based on predefined functions where analyses of these functions are later performed while no predefined function is considered for GP approach. GP is believed to be powerful with respect to regression techniques and neural networks and has proven to be an effective tool to model and obtain clear formulations of experimental studies including multivariate problems with no existing analytical models [28][29][30][31][32].…”

Section: Genetic Programming Structures and Parametersmentioning

confidence: 99%

Toughness prediction in functionally graded Al6061/SiCp composites produced by roll-bonding

Pouraliakbar¹,

Monazzah

Bagheri

et al. 2014

Ceramics International

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Stress Corrosion Cracking Resistant Aluminum Alloys: Optimizing Concentrations of Alloying Elements and Tempering

Cited by 21 publications

References 15 publications

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

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

Toughness prediction in functionally graded Al6061/SiCp composites produced by roll-bonding

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