MultOpt++: a fast regression-based model for the constraint violation fraction due to composition uncertainties

Müller A, Sprenger M, Ritter N, Rettig R, Markl M, Körner C, Singer R (2019)


Publication Language: English

Publication Status: Published

Publication Type: Journal article

Publication year: 2019

Journal

Publisher: IOP PUBLISHING LTD

Book Volume: 27

Journal Issue: 2

DOI: 10.1088/1361-651X/aaf01e

Abstract

MultOpt++ is a an alloy design tool based on numerical optimization. The concept is similar to approaches in the literature termed 'Alloy by design' and usually dealing with multi-objective optimization problem resulting in a Pareto front with a set of optimal compositions. Unpreventable scattering of element concentrations during the alloy production process causes property deviations of an optimal alloy composition resulting in unfeasible solutions. The violation fractions of such compositions should be taken into account during the optimization process to be able to determine optimal and feasible alloys. Established models for violation fractions require a high computational effort due to a high number of necessary calculations. In this work, we derive a fast model for the constraint violation fraction based on a regression analysis of the mean variation width of alloy properties. We apply this model to nickel-base superalloy properties predicted with the CALPHAD approach. The model allows to select alloys with a lower violation fraction of targeted constraints.

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APA:

Müller, A., Sprenger, M., Ritter, N., Rettig, R., Markl, M., Körner, C., & Singer, R. (2019). MultOpt++: a fast regression-based model for the constraint violation fraction due to composition uncertainties. Modelling and Simulation in Materials Science and Engineering, 27(2). https://dx.doi.org/10.1088/1361-651X/aaf01e

MLA:

Müller, Alexander, et al. "MultOpt++: a fast regression-based model for the constraint violation fraction due to composition uncertainties." Modelling and Simulation in Materials Science and Engineering 27.2 (2019).

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