Property Estimation with Automated Ball Indentation Using Artificial Neural Network and Finite Element Simulation

A combined mechanical property evaluation methodology with abi (automated ball indentation) simulation and artificial neural network (ann) analysis is evolved to evaluate the mechanical properties for material. the experimental load deflection data is converted into meaningful mechanical properties for this material. an ann database is generated with the help of contact type finite element analysis by numerically simulating the abi process for various magnitudes of yield strength (óyp) (200 mpa – 500 mpa) with a range of strain hardening exponent (n) (0.1- 0.5) and strength coefficient (k) (500 mpa – 1500 mpa). for the present problem, a ball indenter of 1.57 mm diameter having young’s modulus approximately 100 times more than the test piece is used to minimize the error due to indenter deformation. test piece dimension is kept large enough in comparison to the indenter configuration in the simulation to minimize the deflection at the outer edge of the test piece. further, this database after the neural network training; is used to analyze measured material properties of different test pieces. the ann predictions are reconfirmed with contact type finite element analysis for an arbitrary selected test sample. the methodology evolved in this work can be extended to predict material properties for any irradiated nuclear material in the service.