Prediction, modeling and characterization of surface texturing by sulfuric etchant on non-toxic titanium bio-material using artificial neural networks and fuzzy logic systems

Abstract Multilayer feed forward network, radial biased function network, generalized regression neural network and adaptive network-based fuzzy inference system (ANFIS) were used to predict the surface roughness of Ti-13Zr-13Nb alloy in etching sulfuric acid. Subsequent processes – polishing, sandblasting and acid etching or SLA – were employed to modify the surface. Alumina particles for surface blasting and concentrated sulfuric acid for acid etching were utilized in this experiment. This was performed for three different periods of time (10, 20 and 30 s) and temperature (25, 45 and 60°C). Correspondingly, the Ti-13Zr-13Nb surfaces were evaluated using a field emission scanning electron microscope for roughening and a contact mode profilometer for the average surface roughness (Ra) (nm). Different configurations of neural networks and ANFIS approaches are examined in order to minimize the root mean square error. Consequently, the ANFIS model is selected by dividing the time and temperature into one and three spaces, respectively, using the Gaussian-shaped membership function. A mathematical model is attained from the best approach in terms of root mean square error to realize the relation of the surface roughness of Ti-13Zr-13Nb alloy in etching sulfuric acid and time and temperature as the effective parameters.