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.
Skip Nav Destination
e-mail: j.a.harding@lboro.ac.uk
e-mail: m.shahbaz@lboro.ac.uk
e-mail: s.srinivas@lboro.ac.uk
e-mail: andrew-kusiak@uiowa.edu
Article navigation
November 2006
Technical Papers
Data Mining in Manufacturing: A Review
J. A. Harding,
J. A. Harding
Wolfson School of Mechanical and Manufacturing Engineering,
e-mail: j.a.harding@lboro.ac.uk
Loughborough University
, Loughborough, Leicestershire LE2 4LA, UK
Search for other works by this author on:
M. Shahbaz,
M. Shahbaz
Wolfson School of Mechanical and Manufacturing Engineering,
e-mail: m.shahbaz@lboro.ac.uk
Loughborough University
, Loughborough, Leicestershire LE2 4LA, UK
Search for other works by this author on:
Srinivas,
Srinivas
Wolfson School of Mechanical and Manufacturing Engineering,
e-mail: s.srinivas@lboro.ac.uk
Loughborough University
, Loughborough, Leicestershire LE2 4LA, UK
Search for other works by this author on:
A. Kusiak
A. Kusiak
Department of Mechanical and Industrial Engineering,
e-mail: andrew-kusiak@uiowa.edu
The University of Iowa
, Iowa City, IA 52242-1527
Search for other works by this author on:
J. A. Harding
Wolfson School of Mechanical and Manufacturing Engineering,
Loughborough University
, Loughborough, Leicestershire LE2 4LA, UKe-mail: j.a.harding@lboro.ac.uk
M. Shahbaz
Wolfson School of Mechanical and Manufacturing Engineering,
Loughborough University
, Loughborough, Leicestershire LE2 4LA, UKe-mail: m.shahbaz@lboro.ac.uk
Srinivas
Wolfson School of Mechanical and Manufacturing Engineering,
Loughborough University
, Loughborough, Leicestershire LE2 4LA, UKe-mail: s.srinivas@lboro.ac.uk
A. Kusiak
Department of Mechanical and Industrial Engineering,
The University of Iowa
, Iowa City, IA 52242-1527e-mail: andrew-kusiak@uiowa.edu
J. Manuf. Sci. Eng. Nov 2006, 128(4): 969-976 (8 pages)
Published Online: December 9, 2005
Article history
Received:
April 4, 2005
Revised:
December 9, 2005
Citation
Harding, J. A., Shahbaz, M., Srinivas, and Kusiak, A. (December 9, 2005). "Data Mining in Manufacturing: A Review." ASME. J. Manuf. Sci. Eng. November 2006; 128(4): 969–976. https://doi.org/10.1115/1.2194554
Download citation file:
Get Email Alerts
Effect of Process Parameters on Texture in Quasi-Isotropic IN718 Processed by Laser Powder Bed Fusion
J. Manuf. Sci. Eng (July 2025)
Related Articles
Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization
J. Mech. Des (April,2008)
A Data Mining Approach for Generation of Control Signatures
J. Manuf. Sci. Eng (November,2002)
Data Mining for Subassembly Selection
J. Manuf. Sci. Eng (August,2004)
Knowledge Discovery in Engineering Applications Using Machine Learning Techniques
J. Manuf. Sci. Eng (September,2022)
Related Proceedings Papers
Related Chapters
The Applications of the Cloud Theory in the Spatial DMKD
International Conference on Electronics, Information and Communication Engineering (EICE 2012)
A Cluster Based Approach for Fuzzy Genetic Data Mining
International Conference on Computer and Automation Engineering, 4th (ICCAE 2012)
Real-Time Prediction Using Kernel Methods and Data Assimilation
Intelligent Engineering Systems through Artificial Neural Networks