Characterization of electrolytic tinplate materials via combined finite element and regression models

In this article, a new method that combines finite element method with data mining techniques is proposed to obtain the mechanical properties of electrolytic tinplate. Using information provided by two simple and economic tests (hardness and spring-back), already used in industries to classify tinplate, yield stress and tensile parameters of a generic electrolytic tinplate can be estimated. Initially, a group of finite element models based on these simple tests were built and validated against experimental data. The validated finite element models were then used to investigate the effect of different thicknesses and electrolytic tinplate plastic hardening parameters. With the convergent results obtained from these finite element simulations, a database was generated with the new electrolytic tinplate properties. Various types of regression models (model trees, artificial neural networks and support vector machines) based on data mining techniques were used to obtain the yield stress and plastic hardening parameters from a generic sample of electrolytic tinplate. The accuracy of the results demonstrates that this new method may be used to economically predict yield stress and plastic hardening parameters of a generic electrolytic tinplate.