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

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.