Quantile regression model for impact toughness estimation

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Abstract

The purpose of this study was to develop a product design model for estimating the impact toughness of low-alloy steel plates. The rejection probability in a Charpy-V test (CVT) is predicted with process variables and chemical composition. The proposed method is suitable for the whole production line of a steel plate mill, including all grades of steel in production. The quantile regression model was compared to the joint model of mean and dispersion and the constant variance model. The quantile regression model proved out to be the most effective method for modelling a highly complicated property at this extent. Next, the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost. © 2010 Springer-Verlag Berlin Heidelberg.

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Tamminen, S., Juutilainen, I., & Röning, J. (2010). Quantile regression model for impact toughness estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6171 LNAI, pp. 263–276). https://doi.org/10.1007/978-3-642-14400-4_21

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