Title

A data mining approach for generation of control signatures

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2002

Journal/Book/Conference Title

Journal of Manufacturing Science and Engineering, Transactions of the ASME

Volume

124

Abstract

Data mining offers methodologies and tools for data analysis, discovery of new knowledge, and autonomous process control. This paper introduces basic data mining algorithms. An approach based on rough set theory is used to derive associations among control parameters and the product quality in the form of decision rules. The model presented in the paper produces control signatures leading to good quality products of a metal forming process. The computational results reported in the paper indicate that data mining opens a new avenue for decision-making in material forming industry.

Keywords

Sustainability, Data mining offers methodologies and tools for data analysis, discovery of new knowledge, and autonomous process control. This paper introduces basic data mining algorithms. An approach based on rough set theory is used to derive associations among control parameters and the product quality in the form of decision rules. The model presented in the paper produces control signatures leading to good quality products of a metal forming process. The computational results reported in the paper indicate that data mining opens a new avenue for decision-making in material forming industry.

Published Article/Book Citation

Journal of Manufacturing Science and Engineering, Transactions of the ASME, 124:4 (2002) pp.923-926.



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URL

http://ir.uiowa.edu/cee_pubs/371