The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An approach to monitoring quality in manufacturing using supervised machine learning on product state data
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Cited by
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
Self-citationJournal 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
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4047855 · Cited by 584
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
Metadata
- Authors
- Wuest, T., Irgens, C., & Thoben, K.-D.
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2013-03-24
- Volume
- 25
- Issue
- 5
- Pages
- 1167-1180
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W1965009340
- Cited by
- 243
- References
- 1
- FWCI
- 10.96
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An approach to monitoring quality in manufacturing using supervised machine learning on product state data. https://app.cochat.ai/papers/10.1007/s10845-013-0761-y
- The paper
- Wuest, T., Irgens, C., & Thoben, K.-D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-013-0761-y
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.
