Spherical indentation is one of the non-destructive techniques that has gained great interest among researchers in recent years. Spherical indentation is inexpensive and quick and can provide a fairly accurate estimation of in-plane residual stresses. This method has typically been used to characterize material properties. However, the application of the technique to measure residual stresses has also been practiced. In this paper, the parameters of Hollomon’s equation and residual stresses were determined using the load–penetration (P–h)curvetechniqueobtainedfromasingleloading-unloadingsphericalindentationtest. Artificial Neural Networks (ANN) were employed to achieve the best results from the data. Neural networks are trained using the data from a series of finite element analyses. An exponential equation is then fitted to the loading curve. Having the fitted equation and the trained neural networks, the residual stresses and the material characteristics were found simultaneously. An important benefit of this method was that it could be used with no requirement for a reference stress-free sample.
