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

Abstract

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 (Ra) and average of maximum peak to valley height of the surface over considered length profile (Rz), 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 Ra, Rz, 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 Ra, Rz, 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, Ra of 0.5 μm is obtained, whereas Ra 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.