Thermal and Thermomechanical Responses Prediction of a Steel Ladle Using a Back-Propagation Artificial Neural Network Combining Multiple Orthogonal Arrays
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In: Steel research international, Vol. 2019, 1900116, 29.04.2019.
Research output: Contribution to journal › Article › Research › peer-review
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TY - JOUR
T1 - Thermal and Thermomechanical Responses Prediction of a Steel Ladle Using a Back-Propagation Artificial Neural Network Combining Multiple Orthogonal Arrays
AU - Hou, Aidong
AU - Jin, Shengli
AU - Harmuth, Harald
AU - Gruber, Dietmar
PY - 2019/4/29
Y1 - 2019/4/29
N2 - To facilitate industrial vessel lining design for various material properties andlining configurations, a method, being composed of the back-propagationartificial neural network (BP-ANN) with multiple orthogonal arrays, isdeveloped, and a steel ladle from secondary steel metallurgy is chosen for acase study. Ten geometrical and material property variations of this steelladle lining are selected as inputs for the BP-ANN model. A total of 160lining configurations nearly evenly distributed within the ten variations spaceare designed for finite element (FE) simulations in terms of five orthogonalarrays. Leave-One-Out cross validation within various combinations oforthogonal arrays determines 7 nodes in the hidden layer, a minimum ratioof 16 between dataset size and number of input nodes, and a Bayesianregularization training algorithm as the optimal definitions for the BP-ANNmodel. The thermal and thermomechanical responses of two optimal liningconcepts from a previous study using the Taguchi method are predicted withacceptable accuracy.
AB - To facilitate industrial vessel lining design for various material properties andlining configurations, a method, being composed of the back-propagationartificial neural network (BP-ANN) with multiple orthogonal arrays, isdeveloped, and a steel ladle from secondary steel metallurgy is chosen for acase study. Ten geometrical and material property variations of this steelladle lining are selected as inputs for the BP-ANN model. A total of 160lining configurations nearly evenly distributed within the ten variations spaceare designed for finite element (FE) simulations in terms of five orthogonalarrays. Leave-One-Out cross validation within various combinations oforthogonal arrays determines 7 nodes in the hidden layer, a minimum ratioof 16 between dataset size and number of input nodes, and a Bayesianregularization training algorithm as the optimal definitions for the BP-ANNmodel. The thermal and thermomechanical responses of two optimal liningconcepts from a previous study using the Taguchi method are predicted withacceptable accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85065156004&partnerID=8YFLogxK
U2 - 10.1002/srin.201900116
DO - 10.1002/srin.201900116
M3 - Article
VL - 2019
JO - Steel research international
JF - Steel research international
SN - 0177-4832
M1 - 1900116
ER -
