Neural network implementation for the prediction of load curves of a flat head indenter on hot aluminum alloy

Abstract The indentation test performed by means of a flat-ended indenter is a valuable non-destructive method for assessment of metals at a local scale. Particularly, from the indentation curves it is possible to achieve several mechanical properties. The aim of this paper is the implementation of an artificial neural network for the prediction of the indentation load as a function of the penetration depth for an aluminium substrate. In particular, the neural network is addressed to the mechanical characterization of the bulk in function of temperature and indentation rate. The results obtained showed a high accuracy in curves prediction.