14 research outputs found
Prediction performance of various numerical model training algorithms in solidification process of A356 matrix composites
129-134This
paper reports the microstructural and mechanical properties of casting Al
matrix composite such as porosity, hardness and tensile strength. The numerical
model and finite element method are applied to simulate the solidification of
the composites. The finite element analysis involves a
number of steps such as finite-element discretization,
imposition of boundary conditions and solution of assembled equations. The
mathematical formulation of this solidification problem is given. The neural
network predictions are directly compared with the experimentally obtained data
to evaluate the learning performance. In this investigation the MAPE is used to
evaluate the performance of model. The results show that Levenberg-Marquardt
learning algorithm
give the best prediction for UTS, hardness and porosity of A356 composite
reinforced with B4C particulates