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

Alexandr Golodnikov, Yevgeny Macheret, A. Alexandre Trindade, Stan Uryasev and Grigoriy Zrazhevsky
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Alexandr Golodnikov: University of Florida
Yevgeny Macheret: Institute for Defense Analysis
A. Alexandre Trindade: University of Florida
Stan Uryasev: University of Florida
Grigoriy Zrazhevsky: University of Florida

A chapter in Cooperative Systems, 2007, pp 269-283 from Springer

Abstract: Summary We extend our earlier work, Golodnikov et al [3] and Golodnikov et al [4], by estimating the entire probability distributions for the impact toughness characteristic of steels, as measured by Charpy V-Notch (CVN) at −84°C. Quantile regression, constrained to produce monotone quantile function and unimodal density function estimates, is used to construct the empirical quantiles as a function of various alloy chemical composition and processing variables. The estimated quantiles are used to produce an estimate of the underlying probability density function, rendered in the form of a histogram. The resulting CVN distributions are much more informative for alloy design than singular test data. Using the distributions to make decisions for selecting better alloys should lead to a more effective and comprehensive approach than the one based on the minimum value from a multiple of the three test, as is commonly practiced in the industry.

Keywords: Impact Toughness; Efficient Frontier; Quantile Function; Estimate Distribution; Conditional Probability Density (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-540-48271-0_16

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

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