Predicting materials properties and behavior using classification and regression trees

✍ Scribed by Yong Li

• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 363 KB
• Volume: 433
• Category: Article
• ISSN: 0921-5093
• DOI: 10.1016/j.msea.2006.06.100

✦ Synopsis

An investigation was conducted to evaluate the effectiveness of a non-parametric statistical methodology of classification and regression tree (CART) [L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth Inc., California, 1984] as an alternative to the traditional parametric-based regression techniques in predicting materials properties and behavior. It has been demonstrated, with its application to a database on the creep rupture data of austenitic stainless steels, that the CART technique consistently outperforms the conventional curvilinear regression method in terms of the accuracy of prediction. Moreover, the results of the CART analysis provide an insight into the relationships and interactions between the materials variables and the insight will be beneficial to understanding materials behavior and useful in materials design.