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TECHNICAL PAPERS

Data Mining in Manufacturing: A Review

[+] Author and Article Information
J. A. Harding

Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE2 4LA, UKj.a.harding@lboro.ac.uk

M. Shahbaz

Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE2 4LA, UKm.shahbaz@lboro.ac.uk

Srinivas

Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE2 4LA, UKs.srinivas@lboro.ac.uk

A. Kusiak

Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242-1527andrew-kusiak@uiowa.edu

J. Manuf. Sci. Eng 128(4), 969-976 (Dec 09, 2005) (8 pages) doi:10.1115/1.2194554 History: Received April 04, 2005; Revised December 09, 2005

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

History of manufacturing applications of data mining

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Figure 2

Engineering design literature over time

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Figure 3

Manufacturing systems literature over time

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Figure 4

Decision support systems literature over time

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Figure 5

Shop floor control and layout literature over time

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Figure 6

Fault detection and quality improvement literature over time

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Figure 7

Maintenance literature over time

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Figure 8

Customer relationships management literature over time

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