Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement
Author:
Funder
SODERCAN
EU - FEDER
GLOBAL STEEL WIRE
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-020-01623-9.pdf
Reference42 articles.
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2. Atkinson, H. V., & Shi, G. (2003). Characterization of inclusions in clean steels: A review including the statistics of extremes methods. Progress in Materials Science, 48(5), 457–520. https://doi.org/10.1016/S0079-6425(02)00014-2.
3. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92 (pp. 144–152). https://doi.org/10.1145/130385.130401.
4. Çaydaş, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639–650. https://doi.org/10.1007/s10845-010-0415-2.
5. Chen, S., Jiang, M., He, X., & Wang, X. (2012). Top slag refining for inclusion composition transform control in tire cord steel. International Journal of Minerals, Metallurgy, and Materials, 19(6), 490–498. https://doi.org/10.1007/s12613-012-0585-3.
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