Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17738
Title: Development of a data mining system for continual process quality improvement
Authors: Tsang, KF
Lau, HCW
Kwok, SK
Keywords: Data mining system
Data warehousing application
Process quality improvement
Slider fabrication
Issue Date: 2007
Publisher: SAGE Publications
Source: Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture, 2007, v. 221, no. 2, p. 179-193 How to cite?
Journal: Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture 
Abstract: 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.
URI: http://hdl.handle.net/10397/17738
ISSN: 0954-4054
EISSN: 2041-2975
DOI: 10.1243/09544054JEM576
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