It is very important to determine microstructure parameters of consolidated
ceramic samples, because it opens new frontiers for further microelectronics
miniaturization and integrations. Therefore, controlling, predicting and
designing the ceramic materials? properties are the objectives in ceramic
materials consolidating process, within the science of sintering. In order
to calculate the precise values of desired microstructure parameter at the
level of the grains? coating layers based on the measurements on the bulk
samples, we applied the artificial neural networks, as a powerful
mathematical tool for mapping input-output data. Input signals are
propagated forward, as well as the adjustable coefficients that contribute
the calculated output signal, denoted as error, which is propagated
backwards and replaced by examined parameter. In our previous research, we
used neural networks to calculate different electrophysical parameters at
the nano level of the grain boundary, like relative capacitance, breakdown
voltage or tangent loss, and now we extend the research on sintered
material?s density calculation. Errors on the network output were
substituted by different consolidated samples density values measured on the
bulk, thus enabling the calculation of precise material?s density values
between the layers. We performed the neural network theoretical experiments
for different number of neurons in hidden layers, according to experimental
ceramics material?s density of ?=5.4x103[kg/m3], but it opens the
possibility for neural networks application within other density values, as
well.