Estimating the probability distributions of alloy impact toughness: A constrained quantile regression approach

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationCooperative Systems
Subtitle of host publicationControl and Optimization
EditorsDon Grundel, Panos Pardalos, Robert Murphey, Oleg Prokopyev
Pages269-283
Number of pages15
DOIs
StatePublished - 2007

Publication series

NameLecture Notes in Economics and Mathematical Systems
Volume588
ISSN (Print)0075-8442

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