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

• Published in: Scientific reports Vol. 12; no. 1; p. 7184
• Main Authors: Sadati Tilebon, Seyyed Mohamad, Emamian, Seyed Amirhossein, Ramezanpour, Hosseinali, Yousefi, Hashem, Özcan, Mutlu, Naghib, Seyed Morteza, Zare, Yasser, Rhee, Kyong Yop
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/61/54; 639/166/985; Dental Implants; Dental prosthetics; Etching; Humanities and Social Sciences; Humans; Mathematical models; multidisciplinary; Neural networks; Neural Networks, Computer; Science; Science (multidisciplinary); Sulfuric acid; Temperature; Titanium
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-11254-0

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% H 2 SO 4 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/cm 2 . 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.