A Neural Networks approach to characterize material properties using the spherical indentation test

AbstractDetermination of material characteristics using the instrumented indentation test has gained interests among many researchers. The output of a spherical indentation test is usually the load-penetration (P-h) curve. To achieve this goal, the elastic deformation of sphere must be eliminated from the penetration. To determine three parameters of the LUDWIG's equation which are σy, K and m, choice of a prompt numerical procedure is of essences.The purpose of the present work is to determination three parameters of the LUDWIG's equation using the spherical indentation test and Neural Networks. Therefore, a Neural Networks is trained following the spherical indentation test using two parameters that are obtained from the P-h curve. The output of the networks is the three parameters of the LUDWIG's equation. The results were then compared with the finite element predictions and verified using the experimental data. A good agreement was observed. Finally, the weights of Neural Networks layer were extracted for easy use of the above procedure