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.
A Data Mining Approach for Generation of Control Signatures
Contributed by the Manufacturing Engineering Division for publication in the Journal of Manufacturing Science and Engineering. Manuscript received February 2001; Revised January 2002. Associate Editor. S. J. Hu.
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Kusiak, A. (October 23, 2002). "A Data Mining Approach for Generation of Control Signatures ." ASME. J. Manuf. Sci. Eng. November 2002; 124(4): 923–926. https://doi.org/10.1115/1.1511524
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