Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21631
Title: An intelligent production workflow mining system for continual quality enhancement
Authors: Ho, GTS
Lau, HCW
Lee, CKM
Ip, AWH 
Pun, KF
Keywords: Continual quality enhancement
Fuzzy logic
Neural continual quality enhancement
Neural networks
Online analytical processing
Production workflow
Issue Date: 2006
Publisher: Springer
Source: International journal of advanced manufacturing technology, 2006, v. 28, no. 7-8, p. 792-809 How to cite?
Journal: International journal of advanced manufacturing technology 
Abstract: In today's globally competitive industries, high-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way to survive in an enterprise. Studies indicate that automating quality audits and adding decision support in quality improvement is an attractive idea. In this environment, production workflow mining is an approach for extracting knowledge from different manufacturing processes in order to assist real-time quality prediction and improvement. This papers attempts to propose an intelligent production workflow mining system (IPWMS) embracing online analytical processing (OLAP) and data mining technology, together with the use of artificial intelligence combining artificial neural networks (ANNs) and fuzzy rule sets to realize knowledge discovery and decision support in high-quality manufacturing. To validate the feasibility of the proposed system, a prototype is developed and evaluated in a company, and a description of this case example is covered in this paper.
URI: http://hdl.handle.net/10397/21631
ISSN: 0268-3768
EISSN: 1433-3015
DOI: 10.1007/s00170-004-2416-9
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