MODEL FOR PREDICTING THE MECHANICAL PROPERTIES OF LOW CARBON STEEL

Abstract

There has been constant development in the area of mechanical properties of low carbon steel but many of the approaches used to determine the properties have always been through experimentation processes. This work presents another approach which is a modelling approach whereby determination of the mechanical properties of any low carbon steel of a known thickness can be done without performing any experiment. This work focuses on some major mechanical properties which are ultimate tensile strength, yield strength, hardness and impact. The experimental data obtained from the mechanical tests carried out on steel samples were used to formulate the model. Polynomial regression analysis was used as the algorithm to formulate the model and JavaScript programming language was used to develop simulation software called LCS-4Tell for the model. Data from the model which are the predicted values for the mechanical properties were validated using t-test paired with mean, correlation coefficient and standard error analysis. The result of the model data validation shows a high correlation coefficient which is a perfect positive and strong t-value in the case of the t-test paired analysis. This proved the authenticity, work ability and capability of the model. The model is capable of predicting the thickness value of any low carbon steel between the carbon content range of 0.01% and 0.25%. It is believed that this model provides a means by which the mechanical properties of low carbon steel can be determined without going through experimentation processes.