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Data Mining in Manufacturing: A Review
Abstract
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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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
Machine learning in manufacturing: Advantages, challenges, and applications
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016) · Production & Manufacturing Research · doi:10.1080/21693277.2016.1192517 · Cited by 1,319
Machine Learning for industrial applications: A comprehensive literature review
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114820 · Cited by 553
Short-Term Prediction of Wind Farm Power: A Data Mining Approach
Kusiak, A., Zheng, H., & Song, Z. (2009) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2008.2006552 · Cited by 339
Self-citationData-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., et al. (2021) · AIAA Journal · doi:10.2514/1.j060131 · Cited by 270
Metadata
- Authors
- Harding, J. A., Shahbaz, M., Srinivas, & Kusiak, A.
- Venue
- Journal of Manufacturing Science and Engineering
- Publisher
- ASME International
- Published
- 2005-12-09
- Volume
- 128
- Issue
- 4
- Pages
- 969-976
- ISSN
- 1087-1357 (ISSN-L)
- 1528-8935
- OpenAlex
- W2143515036
- Cited by
- 509
- References
- 0
- FWCI
- 30.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Harding, J. A., Shahbaz, M., Srinivas, & Kusiak, A. (2006). Data Mining in Manufacturing: A Review. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.2194554
Data sources
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.
