Estimating the Probability Distributions of Alloy Impact Toughness: a Constrained Quantile Regression Approach

Golodnikov, A. N.; Macheret, Yevgeny; Trindade, A. Alexandre; Uryasev, Stan; Zrazhevsky, Grigoriy

doi:10.1007/978-3-540-48271-0_16

Cited by 2 publications

(3 citation statements)

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“…We have succeeded in implementing this approach, building on the results in this paper. The findings, presented in Golodnikov et al (2005b), show this to be a viable option.…”

Section: Introductionmentioning

confidence: 82%

“…Such a capability can lead to a greater reduction in the cost of experimental programmes, shifting resources for the development of optimized alloys into more cost-effective computer modelling techniques. Furthermore, the proposed computer modelling approach has the potential to aid in the design of optimized alloys by conducting additional computer simulations, as demonstrated by Golodnikov et al (2005b). This has important implications for the design of physical experiments targeting the discovery of new and improved alloys.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, these models permit us to optimize the chemical composition and processing parameters, leading to a very efficient approach for alloy development that integrates experimental data with statistical modelling. The optimization simulations based on these regression models are presented in Golodnikov et al (2005b). They demonstrate that although steel 23 is very competitive, an improvement of the order of 20% for Yield or 30% for the lowest value of CVN can still (theoretically) be attained by successive refinements of the experimental process.…”

Section: Use Of the Regression Models For Ranking Steelsmentioning

confidence: 99%

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Statistical modelling of composition and processing parameters for alloy development

Golodnikov

Macheret

Trindade

et al. 2005

Modelling Simul. Mater. Sci. Eng.

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We propose the use of regression models as a tool to reduce time and cost associated with the development and selection of new metallic alloys. A multiple regression model is developed which can accurately predict tensile yield strength of high strength low alloy steel based on its chemical composition and processing parameters. Quantile regression is used to model the fracture toughness response as measured by Charpy V-Notch (CVN) values, which exhibits substantial variability and is therefore not usefully modelled via standard regression with its focus on the mean. Using Monte-Carlo simulation, we determine that the three CVN values corresponding to each steel specimen can be plausibly modelled as observations from the 20th, 50th and 80th percentiles of the CVN distribution. Separate quantile regression models fitted at each of these percentile levels prove sufficiently accurate for ranking steels and selecting the best combinations of composition and processing parameters.

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“…We have succeeded in implementing this approach, building on the results in this paper. The findings, presented in Golodnikov et al (2005b), show this to be a viable option.…”

Section: Introductionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Use Of the Regression Models For Ranking Steelsmentioning

confidence: 99%

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