Abstract
This article examines the feasibility of predicting the mechanical properties of hot-rolled steel in flat-rolled-products shop No. 10 with the use of artificial neural networks. The mechanical properties are determined by means of a mathematical model constructed on the basis of data on the chemical composition of heats with allowance for the nominal thickness of the finished product, finishing temperature, and coiling temperature. Implementation of the findings is making it possible to reduce labor costs incurred in sample preparation, reduce metal waste, determine the soundness of batches while strip is being coiled, and eliminate the problems of the statistics-based nondestructive testing method currently in use.
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