The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Data mining in manufacturing: a review based on the kind of knowledge
Open accessAbstract
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.
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Cites
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Cited by
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
Journal of Intelligent Manufacturing
(2013) · Journal of Intelligent Manufacturing · doi:10.1007/10845.1572-8145 · Cited by 917
Machine learning and data mining in manufacturing
Dogan, A., & Birant, D. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114060 · Cited by 677
Data mining in the construction industry: Present status, opportunities, and future trends
Yan, H., Yang, N., Peng, Y., & Ren, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103331 · Cited by 161
A three level hierarchical architecture for an efficient storage of Industry 4.0 data
Villalobos, K., Ramírez-Durán, V. J., Diez, B., Blanco, J. M., Goñi, A., & Illarramendi, A. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103257 · Cited by 35
Metadata
- Authors
- Choudhary, A. K., Harding, J. A., & Tiwari, M. K.
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2008-07-15
- Volume
- 20
- Issue
- 5
- Pages
- 501-521
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W2071909685
- Cited by
- 494
- References
- 60
- FWCI
- 44.55
- Type
- journal article
- Language
- en
- Open access
- green
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- Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-008-0145-x
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
