Predicting assembly quality of complex structures using data mining: Predicting with decision tree algorithm

Abstract

Our research aims at obtaining the relevant factors that cause the decrease in quality of assembly parts. Decision tree technique was employed to induce useful information hidden within a vast collection of data. The major objective of this study was to classify the existing data into certain types of segmentations and then predict the behaviour of a ball joint assembly. The intervals of the rolling time and achieved rolling force leading to occurrence of the high moment values of the ball joint during testing stage have been found. © 2006 International Federation for Information Processing.