A Predictive Model of Hot Rolling Flow Stress by Multivariate Adaptive Regression Spline

A new modeling method called multivariate adaptive regression spline (MARS) was firstly employed to predict the hot rolling flow stress and explain the relationship among flow stress and various parameters such as major chemical compositions, rolling temperature, rolling speed, compression ratio, th...

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Bibliographic Details
Published inMaterials science forum Vol. 898; pp. 1148 - 1155
Main Authors Yu, Wan Huan, Yao, Chang Gui, Yi, Xiang De
Format Journal Article
LanguageEnglish
Published Pfaffikon Trans Tech Publications Ltd 19.06.2017
Subjects
Chemical composition
Compression ratio
Deformation
Errors
Heat treating
Hot rolling
Mathematical models
Modelling
Regression analysis
Reliability
Rolling speed
Rolls
Strip
Yield strength
Flow Stress
Predictive Model
Hot Rolling
Multivariate Adaptive Regression Spline
MARS
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Summary:A new modeling method called multivariate adaptive regression spline (MARS) was firstly employed to predict the hot rolling flow stress and explain the relationship among flow stress and various parameters such as major chemical compositions, rolling temperature, rolling speed, compression ratio, thickness, roll radius, furthermore, analyze the importance of the predictor variables. The results showed that the error of training and testing was less than 2%, and rolling temperature, rolling speed, and strip thickness had much contribution to flow stress. Moreover, the impact of various factors on the flow stress can be validated by real production data, which proved the reliability of MARS model to predict the flow stress and guide the practical production.
Bibliography:Selected, peer reviewed papers from the 17th IUMRS International Conference in Asia, (IUMRS-ICA), October 20-24, 2016, Qingdao, China
ISSN:0255-5476
1662-9752
1662-9752
DOI:10.4028/www.scientific.net/MSF.898.1148