Exceedance probability estimation for a quality test consisting of multiple measurements

The purpose of this study was to develop methods for exceedance probability estimation in the case of highly scattered measurement sets. The situation may occur when product quality is verified with several test samples, and thus, traditional point prediction based modelling methods are not sufficient.Density forecasting methods are needed when not only the mean but also the deviance and the distribution shape of the response depend on the explanatory variables. Furthermore, with probability predictors, the ranking methods for the model selection should be chosen carefully, when models trained with different methods are compared.In this article, the impact toughness of the steel products was modelled. The rejection probability in Charpy-V quality test was predicted with mean and deviation models, distribution shape model and quantile regression model. The proposed methods were employed in two steel manufacturing applications with different distributional properties.

► An effective transformation for multiple measurements test sets, which improves the modelling performance.
► Rejection probability prediction instead of the mean, which enables the quality improvement.
► The use of density forecasts instead of the point forecasts proved to be important especially with datasets with high diversity.
► The selection of model ranking methods was critical, because of differences between the models.