Design of Alloy's Concentrations for Optimized Strength, Temperature, Time-to-Rupture, Cost and Weight

Abstract

A novel method has been developed and experimentally verified that can enable a significant part of the steel alloy development procedure to be performed computationally by using the power of a true mathematical evolutionary multi-objective optimization algorithm. During the alloy optimization process, maximized operating temperature, tensile stress, time-to-rupture, and minimized cost and weight were treated as simultaneous often conflicting objectives. Concentrations of most important alloying elements were predicted so that new alloys have the best multiple properties. This alloy design concept was verified using strictly experimental data. The number of required experimental evaluations of the candidate alloys with this optimization approach is very low. This approach has the potential of identifying the new chemical compositions of significantly superior steel alloys with only a few hundred alloy samples.

Optimizing chemistry of bulk metallic glasses for improved thermal stability

“…Specifically, we are currently concentrating on simultaneously maximizing T g , T l and T g /T l and minimizing density of Zr-based BMGs [18,19]. The proposed optimization method is based on combining experimentally obtained multiple properties of the BMGs and a computational optimization algorithm [20][21][22][23][24] rather than on traditional experimentation alone, expert experience and intuition.…”

“…Specifically, the proposed BMG design method combines an advanced stochastic multiobjective evolutionary optimization algorithm based on self-organizing graph theory and a self-adapting response surface methodology [22,25]. During the iterative computational design procedure, a small set of new BMG alloys is periodically predicted, manufactured and experimentally evaluated for their properties in order to continuously verify the accuracy of the entire design methodology [20][21][22][23][24]. The proposed BMG alloy design optimization method is thus experimentally verified.…”

“…Because of its proven robustness, computing speed and versatility we have decided to use the multi-objective constrained indirect optimization based upon self-organization (IOSO) algorithm [18][19][20][21][22][23][24]25]. This multi-objective optimization algorithm allows for concentrations of the alloying elements to be optimized so that several of the BMG alloy properties (maximizing T g , T l and T g /T l and minimizing density) are simultaneously extremized, while satisfying a number of equality and inequality constraints (minimum and maximum specified concentrations for each of the alloying elements).…”

“…IOSO is a semi-stochastic multi-objective optimization algorithm incorporating certain aspects of a selective search on a continuously updated multi-dimensional response surface [18][19][20][21][22][23][24][25]30]. Evaluations of objective functions (T g , T l , T g /T l and density) in this particular project were obtained utilizing experimental testing and verification of the BMG samples in order to determine optimum concentrations of each of the alloying elements.…”

MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrations

“…The sensitivity of the alloy properties is rarely been taken into account [18] during complex optimization processes because of the lack of fast models for the property robustness, which is defined as the inverse of the sensitivity. The general concept of multi-objective robust optimization was initially developed by Deb et al [19] from single-objective robust optimization [20,21], and it is has been summarized in detail by Beyer et al [22].…”