Modelling the effect of chemical elements on the mechanical properties of B420C construction steels with statistical analysis

Studies on the effects of alloying elements on the mechanical properties of steels have been carried out mainly for steels containing high amounts of alloying elements. On the other hand, studies on the effects of alloying elements on the mechanical properties of steels containing low amounts of alloying elements are limited. Since its chemical composition changes in a a very broad range when scrap is used as a raw material to produce B420C construction steel, which is a low-alloy steel, it is necessary to strictly control the change in chemical composition throughout the production process in order to obtain the targeted mechanical properties, This requirement complicates the production process and causes production costs to increase. The main subject of the study is to determine the effect of the type and amount of alloying elements present in the steel composition on the mechanical properties (yield strength, tensile strength and % elongation) of B420C construction steels by modeling them with statistical methods. It has been determined that carbon, manganese, chromium and sulfur elements have a significant effect on the yield strength, tensile strength and % elongation of B420C construction steels. Because it affects mechanical properties as well, the unit weight and the cooling rate of the product were also added to the list of independent variables in the statistical models. Results obtained indicate that the effects of these elements on the above mentioned mechanical properties can be predicted in a way that is compatible with the results obtained by experimental methods. Using the models developed in this study; it has been found that yield strength, tensile strength and % elongation values of B420C construction steels produced using scrap obtained from various different sources and thus having different chemical compositions can be predicted in compliance with the relevant standard and without performing experimental tests. Thus reducing production costs.