Steel slab surface quality prediction using neural networks

Abstract

Columbus Stainless grinds the majority of the steel slabs that are produced to improve the surface quality. However, the surface quality of some slabs is good enough not to be ground. If a reliable method can be found to identify these slabs, the production costs associated with grinding can be saved. Initially slabs were selected manually based on knowledge of the process parameters that affect the steel surface quality. This was not successful and may have been due to the interaction between variables and non-linear effects that were not taken into account. A neural network approach was therefore considered. A multilayer perceptron neural network was used for defect prediction. The neural network is trained by repeatedly attempting to match input data to the corresponding output data. Linear regression and decision tree models were also trained for comparison. The neural networks performed the best. The effectiveness of the models was tested using a test data set (data not used during the training of the model) and the neural networks gave high levels of accuracy (greater than 75% for both defect and no-defect cases). A committee of models was also trained, but this did not improve the prediction accuracy. Neural networks provided a powerful tool to predict the slab surface quality. This has enabled Columbus Stainless to limit the deterioration in the steel quality associated with non-grinding of slabs.