Although the principles of microalloying are well-established, the complexity of thermomechanical processing is such that it is difficult to deconvolute the contribution to strength of the microalloying additions as a function of the many variables involved. We report in this paper the analysis of a large database on hot-rolled steels to create a neural network model which estimates the strength as a function of chemical composition and process variables. This model is then used to make comparisons against equivalent data in order to realise the role of minute additions of carbide formers in changing the properties of steels.
Materials and Manufacturing Processes 24 (2009) 1-7
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