Defect Cause Modeling with Decision Tree and Regression Analysis

BAKIR B. S., BATMAZ İ., Gunturkun F. A., Ipekci T. A., KÖKSAL G., ÖZDEMİREL N. E.

Conference of the World-Academy-of-Science-Engineering-and-Technology, Cairo, Egypt, 8 - 10 December 2006, vol.17, pp.266-268 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 17
  • City: Cairo
  • Country: Egypt
  • Page Numbers: pp.266-268
  • Keywords: Casting industry, decision tree algorithm C5.0, logistic regression, quality improvement, LIKELIHOOD
  • TED University Affiliated: No


The main aim of this study is to identify the most influential variables that cause defects on the items produced by a casting company located in Turkey. To this end, one of the items produced by the company with high defective percentage rates is selected. Two approaches-the regression analysis and decision trees-are used to model the relationship between process parameters and defect types. Although logistic regression models failed, decision tree model gives meaningful results. Based on these results, it can be claimed that the decision tree approach is a promising technique for determining the most important process variables.