Intelligent modeling and optimization of titanium surface etching for dental implant application

Acid-etching is one of the most popular processes for the surface treatment of dental implants. In this paper, acid-etching of commercially pure titanium (cpTi) in a 48% H₂SO₄ solution is investigated. The etching process time (0-8 h) and solution temperature (25-90 °C) are assumed to be the most effective operational conditions to affect the surface roughness parameters such as arithmetical mean deviation of the assessed profile on the surface (R_a) and average of maximum peak to valley height of the surface over considered length profile (R_z), as well as weight loss (WL) of the dental implants in etching process. For the first time, three multilayer perceptron artificial neural network (MLP-ANN) with two hidden layers was optimized to predict R_a, R_z, and WL. MLP is a feedforward class of ANN and ANN model that involves computations and mathematics which simulate the human-brain processes. The ANN models can properly predict R_a, R_z, and WL variations during etching as a function of process temperature and time. Moreover, WL can be increased to achieve a high Ra. At WL = 0, R_a of 0.5 μm is obtained, whereas R_a increases to 2 μm at WL = 0.78 μg/cm². Also, ANN model was fed into a nonlinear sorting genetic algorithm (NSGA-II) to establish the optimization process and the ability of this method has been proven to predict the optimized etching conditions.