Artificial Neural Networks Based Approaches for the Prediction of Mean Flow Stress in Hot Rolling of Steel

2017 ◽  
pp. 626-637
Author(s):  
Marco Vannucci ◽  
Valentina Colla ◽  
Vincenzo Iannino
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Stress ◽  
Hot Rolling ◽  
Mean Flow ◽  
Mean Flow Stress ◽  
Artificial Neural

New Phenomenological Model of Hot Mean Flow Stress

2013 ◽  
Vol 32 (2) ◽  
pp. 133-138
Author(s):  
Ivo Schindler ◽  
Petr Kawulok ◽  
Stanislav Rusz ◽  
Jiří Plura ◽  
Zdeňek Vašek ◽  
...  
Keyword(s):  
Flow Stress ◽  
Hot Rolling ◽  
Effective Strain ◽  
Single Equation ◽  
Mean Flow ◽  
Strain And Strain Rate ◽  
Wide Range ◽  
Mean Flow Stress ◽  
Experimental Values ◽  
Fast Prediction

AbstractBased on the measurement of roll forces during the laboratory hot rolling of flat samples graded in thickness, the new phenomenological type of mean flow stress model was developed and applied on plain-carbon and HSLA steels. The obtained models describe with a very good accuracy the hot deformation resistance characteristics in the temperature range 1123 to 1463 K, large effective strain, and strain rate in the useful range of approximately 10 to 100 s−1. Difficulty in the mathematical description of the influence of temperature on mean flow stress in the wide range of temperature by a single equation was solved by introducing another constant in the temperature member of the conventional model. The newly proposed model solves by phenomenological means a frequent problem of heteroscedasticity of relative deviations between the calculated and experimental values of mean flow stress values depending on temperature. It becomes more reliable from the viewpoint of the operational application, e.g. fast prediction of mean flow stress values and power/force parameters necessary in the steering systems of hot rolling mills.

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