Development of a data mining system for continual process quality improvement

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
View graph of relations



Original languageEnglish
Pages (from-to)179-193
Journal / PublicationProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Issue number2
Publication statusPublished - 2007
Externally publishedYes


Quality issues are important to manufacturers so that they can ensure their products are up to the highest standard. However, due to various causes associated with the operational processes, it is hard to identify all the sources of quality problems in a timely manner. A product's defects may cause a very serious loss in market share and low profit margin in the competitive environment of short product life cycles. In order to address this, a systemized and well-designed approach has been formulated to perform the desired task of problem identification based on the specific process. A data mining system (DMS), which is incorporated into emerging technologies including on-line analytical processing (OLAP) and decision tree-based artificial neural networks (ANNs), is presented. With this DMS, essential support for users who wish to identify the cause and source of problems can be provided so that immediate action can be taken for their rectification. The system prototype has been implemented in the factory of a manufacturer of magnetic heads for hard disk drives (HDDs) in order to validate the workability of the proposed methodology. © IMechE 2007.

Research Area(s)

  • Data mining system, Data warehousing application, Process quality improvement, Slider fabrication

Citation Format(s)