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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 362601, 9 pages
Research Article

Ameliorated Austenite Carbon Content Control in Austempered Ductile Irons by Support Vector Regression

1Department of Electrical Engineering, National Taipei University, San Shia District, New Taipei City 23741, Taiwan
2Mechanical Engineering and Chemical Technology, British Columbia Institute of Technology, BC, Canada
3Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Received 3 February 2013; Accepted 5 April 2013

Academic Editor: Chang-Hua Lien

Copyright © 2013 Chan-Yun Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Austempered ductile iron has emerged as a notable material in several engineering fields, including marine applications. The initial austenite carbon content after austenization transform but before austempering process for generating bainite matrix proved critical in controlling the resulted microstructure and thus mechanical properties. In this paper, support vector regression is employed in order to establish a relationship between the initial carbon concentration in the austenite with austenization temperature and alloy contents, thereby exercising improved control in the mechanical properties of the austempered ductile irons. Particularly, the paper emphasizes a methodology tailored to deal with a limited amount of available data with intrinsically contracted and skewed distribution. The collected information from a variety of data sources presents another challenge of highly uncertain variance. The authors present a hybrid model consisting of a procedure of a histogram equalizer and a procedure of a support-vector-machine (SVM-) based regression to gain a more robust relationship to respond to the challenges. The results show greatly improved accuracy of the proposed model in comparison to two former established methodologies. The sum squared error of the present model is less than one fifth of that of the two previous models.