Enhancing product quality of a process

Data mining (DM) is used to improve the performance of manufacturing quality control activity, and reduces productivity loss. The purpose of this paper is to discover useful hidden patterns from fabric data to reduce the amount of defective goods and increase overall quality.

This research examines the improvement of manufacturing process via DM techniques. The paper explores the use of different preprocessing and DM techniques (rough sets theory, attribute relevance analysis, anomaly detection analysis, decision trees and rule induction) in carpet manufacturing as the real world application problem. SPSS Clementine Programme, Rosetta Toolkit, ASP (Active Server Pages) and VBScript programming language are used.

The most important variables of attributes that are effective in product quality are determined. A decision tree (DT) and decision rules are generated. Therefore, the faults in the process are detected. An on‐line programme is generated and the model's results are used to ensure the prevention of faulty products.

In time, this model will lose its validity. Therefore, it must be redeveloped periodically.

This study's productivity can be increased especially with the help of artificial intelligence technology. This research can also be applied to different industries.

The size and complexity of data make extraction difficult. Attribute relevance analysis is proposed for the selection of the attribute variables. The knowledge discovery in databases process is used. In addition, the system can be followed on‐line with this interactive ability.