SIMULATION OF TECHNOLOGICAL PROCESS BY USAGE NEURAL NETWORKS AND FACTORIAL DESIGN OF EXPERIMENTS

Abstract

The possibilities of simulation of technological process of aluminium anodic oxidation using the methodology of Design of Experiments (DOE) and theory of neural networks in order to monitor the anodizing process under various operating conditions are presented in this paper. The influence of chemical and physical input factors on the resulting AAO (anodic aluminium oxide) layer thickness at applied current density of 1 A•dm-2 and 6 A•dm-2 has been investigated. Based on the evaluation of experimentally obtained data, the computational predictive model describing the effect of individual input factors and their mutual interactions on the AAO layer thickness was developed in the form of cubic function. This model indicates which factors are important and how they combine to influence the response, it will enable us to optimize operating conditions. The most significant benefit of our research work in this field is the fact that all relevant factors were varied simultaneously.

Recommended articles

VARIANCES OF THE MECHANICAL PROPERTIES OF FILLED POLYMER MATERIALS IN A VARIATION OF INJECTION MOULDING INPUT PARAMETERS

VACLAV CONTOS
Keywords: fiber orientation | injection moulding | rheology | structural analysis | FEM | mesh

TORQUE CHARACTERISTICS OF ROTARY PNEUMATIC MUSCLE ACTUATOR

MARIA TOTHOVA, MILAN BALARA
Keywords: pneumatic muscle actuator | artificial muscle | antagonistic connection | stiffness | torque characteristic

THE DEFORMATION BEHAVIOUR OF HYBRID COMPOSITE SYSTEMS WITH THERMOPLASTIC MATRIXITLE

LUBOS BEHALEK, MARTIN SEIDL, JIRI HABR, PAVEL SEHNOUTEK
Keywords: reinforced polymer s | hybrid composites | mechanical properties | glass fibres | glass spheres | carbon fibres