Prediction of Waviness Values in Skew Rolling Using Machine Learning Methods
| Autor: | Konrad Lis, Zbigniew Pater, Janusz Tomczak, Tomasz Adam Bulzak, Tomasz Kusiak |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Advances in Sciences and Technology, Vol 17, Iss 5, Pp 350-359 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2080-4075 2299-8624 22998624 |
| DOI: | 10.12913/22998624/172338 |
| Popis: | Skew rolling with three rolls is used for producing axisymmetric parts. In this method, the tools are spaced every 120° on the circumference of the workpiece. They are also set askew relative to the rolling axis. Cross sectional reduction is made effective by moving the tapered rolls closer to or away from the center line of the workpiece. Experiments were conducted with variable initial conditions of the rolling process to examine surface topography of rolled parts. Obtained experimental results were then analyzed using machine learning methods in order to determine the most effective regression model with the highest coefficient of determination R2 for waviness prediction. |
| Databáze: | Directory of Open Access Journals |
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