To facilitate industrial vessel lining design for various material properties and
lining configurations, a method, being composed of the back-propagation
artificial neural network (BP-ANN) with multiple orthogonal arrays, is
developed, and a steel ladle from secondary steel metallurgy is chosen for a
case study. Ten geometrical and material property variations of this steel
ladle lining are selected as inputs for the BP-ANN model. A total of 160
lining configurations nearly evenly distributed within the ten variations space
are designed for finite element (FE) simulations in terms of five orthogonal
arrays. Leave-One-Out cross validation within various combinations of
orthogonal arrays determines 7 nodes in the hidden layer, a minimum ratio
of 16 between dataset size and number of input nodes, and a Bayesian
regularization training algorithm as the optimal definitions for the BP-ANN
model. The thermal and thermomechanical responses of two optimal lining
concepts from a previous study using the Taguchi method are predicted with
acceptable accuracy.