The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks
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Citations
Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Hagan, M., Demuth, H. B., Beale, M. H., & Jes?s, O. D. (2016) · Institutional Repository of the National University Open and Distance UNAD (Universidad Nacional Abierta y a Distancia) · Cited by 5,133
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298623 · Cited by 3,917
Predicting surface roughness in machining: a review
Benardos, P. G., & Vosniakos, G.-C. (2003) · International Journal of Machine Tools and Manufacture · doi:10.1016/s0890-6955(03)00059-2 · Cited by 1,079
Tönshoff, H. K., Arendt, C., & Amor, R. B. (2000) · CIRP Annals · doi:10.1016/s0007-8506(07)63455-6 · Cited by 519
SICK, B. (2002) · Mechanical Systems and Signal Processing · doi:10.1006/mssp.2001.1460 · Cited by 439
Development of Empirical Models for Surface Roughness Prediction in Finish Turning
Wang, X., & Feng, C. (2002) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s001700200162 · Cited by 347
Thiele, J. D., & Melkote, S. N. (1999) · Journal of Materials Processing Technology · doi:10.1016/s0924-0136(99)00111-9 · Cited by 305
Risbood, K. A., Dixit, U. S., & Sahasrabudhe, A. (2002) · Journal of Materials Processing Technology · doi:10.1016/s0924-0136(02)00920-2 · Cited by 269
Azouzi, R., & Guillot, M. (1997) · International Journal of Machine Tools and Manufacture · doi:10.1016/s0890-6955(97)00013-8 · Cited by 195
Cited by
Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4036350 · Cited by 677
Asiltürk, İ., & Çunkaş, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.041 · Cited by 391
Evaluating the effect of dataset size on predictive model using supervised learning technique
Ajiboye, A. R., Abdullah-Arshah, R., Qin, H., & Isah-Kebbe, H. (2015) · International Journal of Computer Systems & Software Engineering · doi:10.15282/ijsecs.1.2015.6.0006 · Cited by 104
Metadata
- Authors
- Özel, T., & Karpat, Y.
- Venue
- International Journal of Machine Tools and Manufacture
- Publisher
- Elsevier BV
- Published
- 2005-04
- Volume
- 45
- Issue
- 4-5
- Pages
- 467-479
- ISSN
- 0890-6955 (ISSN-L)
- OpenAlex
- W2162807413
- Cited by
- 698
- References
- 19
- FWCI
- 25.85
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
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- CoChat. (2026). Citation check: Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. https://app.cochat.ai/papers/10.1016/j.ijmachtools.2004.09.007
- The paper
- Özel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture. https://doi.org/10.1016/j.ijmachtools.2004.09.007
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.
