Statistical modelling of composition and processing parameters for alloy development

Alexandr Golodnikov, Yevgeny MacHeret, A. Alexandre Trindade, Stan Uryasev, Grigoriy Zrazhevsky

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish
Pages (from-to)633-644
Number of pages12
JournalModelling and Simulation in Materials Science and Engineering
Issue number4
StatePublished - Jun 1 2005


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