Prediction of Mechanical Properties for High Strength Low Alloyed Steels in a Commercial Hot Dip Galvanizing Line without Soaking Section
Open Access
- 25 April 2020
- Vol. 10 (5), 561
- https://doi.org/10.3390/met10050561
Abstract
The classical thermal cycle in a Hot Dip Galvanizing (HDG) line has four steps: heating, soaking, cooling, and aging. The furnace of an ArcelorMittal HDG line was revamped to increase its heating capacity. This new configuration without the soaking step led to the redefinition of the thermal cycles for all the steel grades, especially for High Strength Low Alloyed (HSLA) steels, where it was necessary to define a new control parameter based on time and temperature. This paper presents the work done to improve the control of the mechanical properties of HSLA steels in the HDG line. Four different types of numerical models (linear and polynomial regressions, artificial neural networks, and Multivariate Adaptive Regression Splines), are applied to predict the yield strength and the tensile strength of individual coils. It is concluded that the introduction of the time–temperature parameter improves the accuracy of the predictions over 10% in most of the cases. An additional improvement is obtained with the use of the process values corresponding to the sampling area instead of the coil average ones. The use of these models makes it possible, if necessary, to adjust individually the presets of the coils before processing them in the galvanizing line and reduce the scattering of the mechanical properties.Keywords
This publication has 26 references indexed in Scilit:
- Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networksIronmaking & Steelmaking: Processes, Products and Applications, 2020
- The consequences of eliminating the soaking section of a commercial hot-dip galvanising line for HSLA production and a control strategy for improving product consistencyIronmaking & Steelmaking: Processes, Products and Applications, 2019
- Designing dual-phase steels with improved performance using ANN and GA in tandemComputational Materials Science, 2018
- The improvement on constitutive modeling of Nb-Ti micro alloyed steel by using intelligent algorithmsMaterials & Design, 2017
- Modeling of microstructure and mechanical properties of heat treated components by using Artificial Neural NetworkMaterials Science and Engineering: A, 2015
- Mapping the input–output relationship in HSLA steels through expert neural networkMaterials Science and Engineering: A, 2006
- Certainty Factor Estimation Using Petri Neural Net for HSLA SteelISIJ International, 2005
- Prediction of mechanical properties of DP steels using neural network modelJournal of Alloys and Compounds, 2004
- Effect of processing parameters on the microstructure and properties of an Nb microalloyed steelMaterials Letters, 2002
- A Mathematical Model to Predict the Mechanical Properties of Hot Rolled C-Mn and Microalloyed Steels.ISIJ International, 1992