Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks

Abstract

Mechanical property prediction is considerably demanded by steel manufacturers because it helps to avoid quality problems and increase productivity. However, prediction of yield strength, tensile strength and elongation of steel after annealing is a hard task because the relation between mechanical properties and process parameters like cold rolling reduction rate, annealing time, annealing temperature and alloying elements of steel is highly nonlinear. Moreover, analytical models mostly depend on experimental constants which are hard to obtain for industrial processes due to the wide range of the process parameters. Using neural network models is more practical, but it may lead to unphysical results. To increase accuracy and avoid unphysical results, analytical annealing models are redeveloped and integrated into the neural network models. Moreover, more than 50,000 tensile test data belonging to the production of past 3 years are used during model construction. Additionally, models are developed for different steel categories based on historical data, and these are aluminum killed, bake hardening, dual phase, high strength low alloy, interstitial free, and rephosphorized steels. According to the results, models have less than 1.7 error for tensile strength, 3.3 error for yield strength and 4.7 error for elongation.