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Data mining in manufacturing: a review based on the kind of knowledge

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journal contribution
posted on 16.05.2012, 09:19 by Alok ChoudharyAlok Choudhary, Jennifer HardingJennifer Harding, Manoj K. Tiwari
In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

CHOUDHARY, A.K., HARDING, J.A. and TIWARI, M.K., 2009. Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20 (5), pp. 501 - 521

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publication date

2009

Notes

This article was published in the serial, Journal of Intelligent Manufacturing [© Springer]. The definitive version is available at: http://dx.doi.org/10.1007/s10845-008-0145-x

ISSN

0956-5515

eISSN

1572-8145

Language

en