The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Artificial intelligence-based sampling planning system for dynamic manufacturing process
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“Existing factory processes will be increasingly subject to analysis to ascertain whether they could be automated (Lee, 2002; Löffler & Tschiesner, 2013; Yang et al., 2017).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Lee, 2002
Citations
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Self-citation
Research works printed but not linked: 4
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Cited by
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
“Existing factory processes will be increasingly subject to analysis to ascertain whether they could be automated (Lee, 2002; Lö ffl er & Tschiesner, 2013; Yang, Chen, Huang, & Li, 2017).”
BackgroundIntegration of decision support systems to improve decision support performance
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An empirical study of intelligent expert systems on forecasting of fashion color trend
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A Literature Review on Sampling Techniques in Semiconductor Manufacturing
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A smart sampling algorithm to minimize risk dynamically
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Case-based reasoning and learning in manufacturing with the TOLTEC planner
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Self-citationSahnoun, M., Bettayeb, B., Bassetto, S.-J., & Tollenaere, M. (2014) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-014-0956-x · Cited by 28
Dynamic management of controls in semiconductor manufacturing
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Automated sampling decision scheme for the AVM system
Cheng, F., Hsieh, Y.-S., Chen, C.-F., & Lyu, J.-R. (2015) · International Journal of Production Research · doi:10.1080/00207543.2015.1072649 · Cited by 11
Performance analysis and optimisation of shape recognition and classification using ANN
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A Smart Sampling Scheduling and Skipping Simulator and its evaluation on real data sets
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Development of Wafer Bonding System for High Precision Bonding Alignment
Kim, T. H., Ahn, D., Lee, M. G., Park, J., & Lee, H.-J. (2024) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-024-01088-3 · Cited by 8
Deep reinforcement learning framework for end-to-end semiconductor process control
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Business process changes on the implementation of artificial intelligence
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Preliminary study of an intelligent sampling decision scheme for the AVM system
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Konzept und Erfahrungsbericht zur Nutzung Künstlicher Intelligenz im Fertigungsmanagement
Mälzer, M., Petersohn, M., Arndt, J., Zimmermann, L., & Ihlenfeldt, S. (2022) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2022-1060
Metadata
- Authors
- Lee, J. H.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Lee, Jang Hee
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2002-02
- Volume
- 22
- Issue
- 2
- Pages
- 117-133
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1975995798
- Cited by
- 31
- References
- 10
- Citations by year
- 2024: 2 · 2022: 1 · 2020: 3 · 2019: 1 · 2015: 2 · 2014: 3 · 2013: 2 · 2012: 2
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- dynamic sampling, sampling planning, semiconductor manufacturing, wafer inspection, sampling strategy optimization, artificial intelligence, wafer bin maps, data mart, cycle time reduction, inspection throughput
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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.
