Journal article
Feature-based rule induction in machining operation using rough set theory for quality assurance
Robotics and computer-integrated manufacturing, v 21(6), pp 559-567
2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
There have been many studies, mainly by the use of statistical modeling techniques, as to predicting quality characteristics in machining operations where a large number of process variables need to be considered. In conventional metal removal processes, however, an exact prediction of surface roughness is not possible or very difficult to achieve, due to the stochastic nature of machining processes. In this paper, a novel approach is proposed to solve the quality assurance problem in predicting the acceptance of computer numerical control (CNC) machined parts, rather than focusing on the prediction of precise surface roughness values. One of the data mining techniques, called rough set theory, is applied to derive rules for the process variables that contribute to the surface roughness. The proposed rule-composing algorithm and rule-validation procedure have been tested with the historical data the company has collected over the years. The results indicate a higher accuracy over the statistical approaches in terms of predicting acceptance level of surface roughness.
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Details
- Title
- Feature-based rule induction in machining operation using rough set theory for quality assurance
- Creators
- Tzu-Liang (Bill) Tseng - Department of Mechanical and Industrial Engineering, The University of Texas at El Paso, El Paso, TX 79968, USAYongjin Kwon - Applied Engineering Technology, Goodwin College of Professional Studies, Drexel University, Philadelphia, PA 19104, USAYalcin M Ertekin - Department of Engineering Technology, Tri-State University, Angola, IN 46703, USA
- Publication Details
- Robotics and computer-integrated manufacturing, v 21(6), pp 559-567
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Technology
- Web of Science ID
- WOS:000232270700006
- Scopus ID
- 2-s2.0-23144447432
- Other Identifier
- 991014878277804721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Manufacturing
- Robotics