A Data Mining Approach for Generation of Control Signatures
Journal of Manufacturing Science and EngineeringPublished 23 October 2002
Andrew Kusiak
Citations24
SJR quartileQ1
SJR score0.70
SNIP0.96
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TL;DR
Basic data mining algorithms are introduced based on rough set theory to derive associations among control parameters and the product quality in the form of decision rules leading to good quality products of a metal forming process.
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
Computer Science
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