Prediction of Waviness Values in Skew Rolling Using Machine Learning Methods
			
	
 
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				Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20–618 Lublin, Poland
				 
			 
										
				
				
		
		 
			
			
		
		
		
		
		
		
	
							
					    		
    			 
    			
    				    					Autor do korespondencji
    					    				    				
    					Konrad  Lis   
    					Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20–618 Lublin, Poland
    				
 
    			
				 
    			 
    		 		
			
																											 
		
	 
		
 
 
Adv. Sci. Technol. Res. J. 2023; 17(5):350-359
		
 
 
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
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.