Predicting mechanical properties of cold rolled low carbon steel based on magnetic parameter measurement using artificial neural network

In this report, the feasibility of predicting mechanical properties using magnetic parameters measurements and artificial neural network (ANN) will be presented. The yield and ultimate tensile strength are predicted by means of two back-propagation neural networks on the basis of hysteresis loop parameter measurements and sample thickness. Inductive measurements are carried out with exciting and pickup coils attached to the magnetizing yoke. Therefore, remanence (B_r), coercive force (H_c), the hysteresis loss (W_h), maximum relative differential permeability (μ_{r max}) and harmonic components of the field and flux density extracted and used as input parameters for training neural network. The individual influence of several input parameters are shown and compared with metallurgical phenomena. The ANN, shown good performance and the results are in agreement with the experimental results. The developed model can be used as an on-line non-destructive evaluation technique.