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.

Microstructure and Mechanical Properties of V-Alloyed Rebars Subjected to Tempcore Process

“…These input variables are the finishing temperature, number of coolers, number of strippers, water pressure, water flow, finishing speed and cooler size relative to the bar size. The most important output variable is achieving the desired mechanical properties [12,13]. Assuming the same chemistry, the primary determining factor of the strength of the bar is the quench depth or the tempered zone.…”

Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

“…We used a PG18 metallurgical microscope and EVO18 scanning electron microscope to observe the sample's microstructure [8]. We also used a THT type high-temperature friction and wear tester to test its wear resistance.…”

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

“…To reduce the production cost and the human intervention, the introduction of automation in the steel industry in a more enhanced form than the present one, is necessary. In this perspective, prediction algorithms are used to speed up the production process, and such efforts are being taken up by the materials science and technology community for attaining certain solutions after compilation of extensive datasets of material composition, process methods, and related properties [14][15][16]. In 2009, Brahme et al have designed an artificial neural-network-based prediction model of cold rolling textures from steel, which is used to predict fiber texture using texture intensities, carbon content, carbide content, and the amount of rolling reduction [17].…”