Optimization of Electrodeposited Hydroxyapatite Coating Parameters on Medical Alloys Based on Artificial Neural Network

CoCrMo Alloys Biometric metal alloys are coated using an electrochemical deposition using electrochemical parameters by a thin layer of hydroxyapatite (HA). In this research, an artificial neural network was applied to optimize the conditions for the deposition of HA coatings with high efficiency in vitro corrosion. Sediment parameters such as sedimentation potential, electrolyte PH. and sedimentation time were selected as input parameters and corrosion potential (Ecorr) as output parameters in the artificial neural network. To train the model, the Multilayer Propeller Neural Network algorithm and pre-sent propagation algorithm are used. After experimenting with many ANN structures, the optimal structure of the model was obtained. The predicted results in a correlation of 0.976 to 0.998 and an absolute error of 1.26% indicate that there is a very good match to the experimental values. In addition, the ANN model became an allergen, and significant inputs influencing the corrosion potential of the specimens were determined.

Keywords:

Modeling , Artificial neural networks , Coating parameters , Electrodeposition