کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10327202 | 680866 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Feature-based rule induction in machining operation using rough set theory for quality assurance
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Robotics and Computer-Integrated Manufacturing - Volume 21, Issue 6, December 2005, Pages 559-567
Journal: Robotics and Computer-Integrated Manufacturing - Volume 21, Issue 6, December 2005, Pages 559-567
نویسندگان
Tzu-Liang (Bill) Tseng, Yongjin Kwon, Yalcin M. Ertekin,