Prediction of mechanical property of steel strips using multivariate adaptive regression splines

被引:10
|
作者
Mukhopadhyay, A. [1 ]
Iqbal, A. [2 ]
机构
[1] Tata Steel, R&D Div, Jamshedpur, Bihar, India
[2] Pioneer Comp P Ltd, Jamshedpur, Bihar, India
关键词
data mining; MARS; property prediction; soft computing; statistics; steel;
D O I
10.1080/02664760802193252
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In recent times, the problem of prediction of properties of a steel strip has attracted enormous attention from different communities such as statistics, data mining, soft computing, and engineering. This is due to the prospective benefits of reduction in testing and inventory cost, increase in yield, and improvement in delivery compliance. The complexity of the problem arises due to its dependency on the chemical composition of the steel, and a number of processing parameters. To predict the mechanical properties of the strip (yield strength, ultimate tensile strength, and Elongation), a model based on multivariate adaptive regression spline has been developed. It is found that the prediction agrees well with the actual measured data.
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页码:1 / 9
页数:9
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