Thesis supervisor: Sándor Pálinkás
Location of studies (in Hungarian): University of Debrecen, Doctoral School of Informatics Abbreviation of location of studies: ITDI
Description of the research topic:
The research work deals with the wear properties of tillage tools as a function of alloy powders with different compositions, thermal dispersion parameters and different soil types. New artificial intelligence methods are used to explore the relationship between input technological and output wear parameters. With the help of the new IT method to be developed, the optimal parameters of the thermal spraying technology, which are important for tool life, will be determined depending on the soil types.
Bibliography
1. Norvig, P.; Russell, S.J. Mesterséges Intelligencia Modern Megközelítésben; 2nd edition; Panem kiadó Kft.: Budapest, HU, 2005
2. Burkov, A. The Hundred-Page Machine Learning Book; 2019; ISBN 9781999579500.
3. David E. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 1989; ISBN 201157675.
4. Davis J R (Ed.): Handbook of Thermal Spray Technology, (ASM International, 2004.)
5. Bach F W, Möhwald K, Laarmann A, Wenz T: Modern Surface Technology,1st edition, (Wiley-vch Verlag, Weinheim, 2006)
6. T. Hastie, R. Tibshirani és J. Friedman, The Elements of Statistical Learning, New York: Springer New York, 2009.
7. J. D. Kelleher, B. M. Namee és A. D'Arcy, Fundamentals of Machine Learning for Predictive Data Analytics, Cambridge, Massachusetts: MIT press, 2015.
8. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer Science and Business Media LLC, New York
Deadline for application: 2024-05-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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