Modelling of Conventional and Severe Shot Peening Influence on Properties of High Carbon Steel via Artificial Neural Network

Abstract

Shot peening (SP), as one of the severe plastic deformation (SPD) methods is employed for surface modification of the engineering components by improving the metallurgical and mechanical properties. Furthermore artificial neural network (ANN) has been widely used in different science and engineering problems for predicting and optimizing in the last decade. In the present study, effects of conventional shot peening (CSP) and severe shot peening (SSP) on properties of AISI 1060 high carbon steel were modelled and compared via ANN. In order to networks training, the back propagation (BP) error algorithm is developed and data of experimental test results are employed. Experimental data illustrates that SSP has superior influence than CSP to improve the properties. Different networks with different structures are trained with try and error process and the one which had the best performance is selected for modeling. Testing of the ANN is carried out using experimental data which they were not used during networks training. Distance from the surface (depth), SP intensity and coverage are regarded as inputs and microhardness, residual stress and grain size are gathered as outputs of the networks. Comparison of predicted and experimental values indicates that the networks are tuned finely and adjusted carefully; therefore, they have good agreement.