Abstract
In recent years, knowledge has received significant attention in manufacturing to built a competitive advantage in the sector. Knowledge induction from data is an important issue in manufacturing to find the failure of the process then predict and improve the future system performance. This research examines the improvement of manufacturing process via data mining. Not only do we detect and isolate machine breakdowns in carpet manufacturing, but also we propose a C4.5 decision tree model. In addition, we use attribute relevance analysis to select the qualitative attribute’s variables. Consequently, manufacturing process is redeveloped.
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