Abstract
In this paper, the mechanical properties of polymer matrix phase (modulus of elasticity, yield stress and work hardening rate) have been determined using combined methods such as nanoindentation, finite element modelling and artificial neural networks. The approach of neural modelling has been employed for the functional approximation of the nanoindentation load-displacement curves. The data obtained from finite element analyses have been used for the artificial neural networks training and validating. The neural model of polymer matrix phase of poly-l-lactide (PLLA) polymer in hydroxyapatite (HAp)/PLLA mechanical behaviour has been developed and tested versus unknown data related to the load-displacement curves that were not used during the neural network training. Based on this neural model, the nanoindentation matrix phase properties of PLLA polymer in HAp/PLLA composite have been predicted.
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