Development of mathematical models for the prediction of mechanical properties of low carbon steel (LCS). (2021) - Borisade, S.G.; Ajibola, O.O.; Adebayo, A.O.; Oyetunji, A.
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 report the development of user friendly JavaScript program based on mathematical models for prediction of mechanical properties such as tensile strength, hardness and impact of low carbon steel; to avoid time consumption, cost and wasted efforts committed to the mechanical testing in laboratory experiments. Experimental data obtained from the mechanical tests carried out on LC-steel samples at varying thicknesses (10 mm, 12 mm 16 mm diameters) were used to formulate the model equations. Polynomial regression analysis was used as the algorithm to express the model and JavaScript programming language was used to develop simulations. Data from the models which are the predicted values for the mechanical properties were validated using t -test paired with mean, correlation coefficient and standard error analyses. The results of the model data validation show 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, workability and capability of the model. Performance of the model is evaluated by the mean error and coefficient of correlation (R). The R value and validation show excellent values showing that JavaScript is appropriate to predict the mechanical properties of low carbon steel. The presented method enables easy analyses and predicting the effects of alloying elements in LC steels (occurring under manufacturing conditions) based on only computer simulation, without necessarily carrying out additional and expensive experimental investigation.