The Research on Measuring Analysis and Prediction of the Nanoscale Precipitated Phase in Steel

The grain size and shape of precipitates in alloyed steel were analyzed and predicted for promoting the refinement effect of nano-nucleation adequately and optimizing the thermal simulation technique. The mapping relationship between thermal simulation parameters (including chemical composition, deformation temperature, and deformation amount and holding time) and nano-precipitates state including grain size, shape and distribution was studied. The weight of BP neural network prediction model was improved by L-M algorithm. The shortage of conventional B-P algorithm such as the slow speed of training, easily come to a local minimum and weak of global search was overcome. By the means of the simulation and practice, the prediction precision of the improved BP neural network on the grain size of nano-precipitates was more than 93%, and the precision of the shape of nano-precipitates was more than 90%.