An intelligent data mining technique for product quality improvement
Karimi, Mahdi, Ferdous, R., Yarlagadda, Prasad K., & Islam, A. (2010) An intelligent data mining technique for product quality improvement. In Butdee, Suthep, Sapsaman, Temsiri, & Yarlagadda, Prasad K. (Eds.) Proceedings of the 10th Global Congress on Manufacturing and Management - Innovative Design for Sustainability In Manufacturing and Management, Global Congress on Manufacturing & Management Board/King Mongkut's University of Technology North , Thailand, Bangkok, pp. 259-268.
Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Keywords:||Manufacturing Process, Data Mining, Quality Improvement|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Database Management (080604)|
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MANUFACTURING ENGINEERING (091000) > Manufacturing Engineering not elsewhere classified (091099)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2010 Global Congress on Manufacturing and Management Board|
|Copyright Statement:||All rights reserved. No part of this publication may be reproduced or distributed in any form or by any means without written permission of the publisher. The content of this publication reflects the work and through of the author(s). Every effort has been made to publish reliable and accurate information as provided by authors. The editors or publisher is not responsible for the validity of the information or for any outcomes resulting from reliance there on.|
|Deposited On:||09 Feb 2011 09:08|
|Last Modified:||01 Mar 2012 00:29|
Repository Staff Only: item control page