Abstract
The main process variables to produce galvanized dual phase (DP) steel sheets in continuous galvanizing lines are time and temperature of intercritical austenitizing (tIA and TIA), cooling rate (CR1) after intercritical austenitizing, holding time at the galvanizing temperature (tG) and finally the cooling rate (CR2) to room temperature. In this research work, the effects of CR1, tG and CR2 on the ultimate tensile strength (UTS), yield strength (YS), and elongation (EL) of cold rolled low carbon steel were investigated by applying an experimental central composite design and a multivariate regression model. A multi-objective optimization and the Pareto Front were used for the optimization of the continuous galvanizing heat treatments. Typical thermal cycles applied for the production of continuous galvanized AHSS-DP strips were simulated in a quenching dilatometer using miniature tensile specimens. The experimental results of UTS, YS and EL were used to fit the multivariate regression model for the prediction of these mechanical properties from the processing parameters (CR1, tG and CR2). In general, the results show that the proposed multivariate model correctly predicts the mechanical properties of UTS, YS and %EL for DP steels processed under continuous galvanizing conditions. Furthermore, it is demonstrated that the phase transformations that take place during the optimized tG (galvanizing time) play a dominant role in determining the values of the mechanical properties of the DP steel. The production of hot-dip galvanized DP steels with a minimum tensile strength of 1100 MPa is possible by applying the proposed methodology. The results provide important scientific and technological knowledge about the annealing/galvanizing thermal cycle effects on the microstructure and mechanical properties of DP steels.
Highlights
The thinner gauge advanced high-strength steels (AHSS) are used in the automotive industry for the manufacture of structural components for the car body
The model coefficients (β) of Equation (1) were determined using the least squares method described by Rencher [38]
The models developed using the multivariate analysis combined with multi-objective optimization shown good prediction capabilities for the mechanical properties of dual phase (DP) steels processed under continuous galvanizing conditions with a prediction error less than 10%
Summary
The thinner gauge advanced high-strength steels (AHSS) are used in the automotive industry for the manufacture of structural components for the car body. The null hypothesis is H0: B1 = 0, where B1 is the matrix X without the first column. The null hypothesis is rejected if Λ ≤ table value and it means that the response variables are influenced by the process variables. The performance of a model can be expressed by the goodness coefficient or determination Metals 2019, 9, 703 coefficient R2. For multivariate modeling there are several measurements of association between the y’s and the x’s. One of these measurements is based on Wilks test: Λ
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