The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
A review of machine learning for the optimization of production processes
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Cites
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,564
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
WU, Z., & HUANG, N. E. (2009) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536909000047 · Cited by 8,715
A study of the behavior of several methods for balancing machine learning training data
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007735 · Cited by 4,203
Surrogate-based analysis and optimization
Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Kevin Tucker, P. (2005) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2005.02.001 · Cited by 2,446
Design and Analysis of Experiments: Special Designs and Applications
(2012) · Wiley Series in Probability and Statistics · doi:10.1002/9781118147634 · Cited by 2,031
Model predictive control: Recent developments and future promise
Mayne, D. Q. (2014) · Automatica · doi:10.1016/j.automatica.2014.10.128 · Cited by 1,975
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
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2301773 · Cited by 1,716
Review of Metamodeling Techniques in Support of Engineering Design Optimization
Wang, G. G., & Shan, S. (2007) · Journal of Mechanical Design · doi:10.1115/1.2429697 · Cited by 1,706
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
Cited by
Hussain, M. (2023) · Machines · doi:10.3390/machines11070677 · Cited by 1,217
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365
A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings
Benedick, P.-L., Robert, J., & Le Traon, Y. (2021) · Sensors · doi:10.3390/s21186195 · Cited by 12
Metadata
- Authors
- Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S.
- Venue
- The International Journal of Advanced Manufacturing Technology
- Published
- 2019-06-20
- Volume
- 104
- Issue
- 5-8
- Pages
- 1889-1902
- ISSN
- 0268-3768 (ISSN-L)
- 1433-3015
- OpenAlex
- W2950694741
- Cited by
- 363
- References
- 75
- FWCI
- 21.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A review of machine learning for the optimization of production processes. https://app.cochat.ai/papers/10.1007/s00170-019-03988-5
- The paper
- Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S. (2019). A review of machine learning for the optimization of production processes. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-019-03988-5
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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.
