Identification of Materials Properties with the Help of Miniature Shear Punch Test Using Finite Element Method and Neural Networks

This paper describes an approach to identify the mechanical properties i.e. fracture and yield strength of steels. The study involves the FE simulation of shear punch test for various miniature specimens thickness ranging from 0.20mm to 0.80mm for four different steels using ABAQUS code. The experimental method of the miniature shear punch test is used to determine the material response under quasi-static loading. The load vs. displacement curves obtained from the FE simulation miniature disk specimens are compared with the experimental data obtained and found in good agreement. The resulting data from the load vs. displacement diagrams of different steels specimens are used to train the neural networks to predict the properties of materials i.e. fracture and yield strength. Two different feed forward neural networks have been created and trained in order to predict the Fracture toughness and yield strength values of different steels. L-M algorithm has been used in the networks to form an output function corresponding to the input vectors used in the network. The trained network provides the output values i.e., fracture toughness and yield strength of unknown input values, which are within in the range of data that is used for the training, of network.