Approximation of material behaviour of PLLA polymer in HAp/PLLA biocomposite material using nanoindentation and finite element method

In this work, a combination of nanoindentation experiment and finite element modeling is proposed in order to obtain approximate material behavior of PLLA polymer in hydroxyapatite (HAp)/poly-L-lactide (PLLA) composite. Knowing these properties is important since the processing conditions used in hot pressing of composites with thermoplastic matrix strongly influence final mechanical properties of material in the solid state. A bi-linear material behavior is adopted for the polymer phase of the HAp/PLLA composite. Material behavior model, stress-strain curve, is determined by modulus of elasticity, yield stress and work-hardening rate. This combined method is proposed to determine material behavior before (modulus of elasticity) and after yield point (work hardening rate) of polymer phase after the hot-pressing of the composite.

Surface characterisation of PLLA polymer in HAp/PLLA biocomposite material by means of nanoindentation and artificial neural networks

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. © 2010 Institute of Materials, Minerals and Mining

Identification and Recognition of Vehicle Environment Using Artificial Neural Networks

Object detection using deep learning over the years became one of the most popular methods for implementation in autonomous systems. Autonomous vehicle requires very reliable and accurate identification and recognition of surrounding objects in real traffic environments to achieve decent detection results. In this paper, special type of Artificial Neural Network (ANN) named Convolutional Neural Network (CNN) was used for identification and recognition of surrounding objects in real traffic. The new model based on CNN was trained and developed to be able to identify and recognize 4 different classes of objects: cars, traffic lights, persons and bicycles. The developed model has shown 94.6% accuracy of object identification and recognizing on the test set