Abstract
A neural network using a learning procedure based on error backpropagation algorithm was employed to identify groups of hot-rolled low carbon steels with homogeneous mechanical characteristics. The mechanical properties of hot-rolled steel bars depend on two categories of variables: technological and structural. Very often, these interact with each other, and the resulting synergic effects are not always easy to evaluate. Moreover, as it is impossible to plan test sequences in which a single parameter at a time is made to vary within a sufficiently wide range, there is no way of designing a theoretical experimental model based on partial multiple regression.
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