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Application of Inverse Neural Networks for Optimal Pretension of Absorbable Mini Plate and Screw System

التفاصيل البيبلوغرافية
العنوان: Application of Inverse Neural Networks for Optimal Pretension of Absorbable Mini Plate and Screw System
المؤلفون: Alex Bernardo Pimentel-Mendoza, Lázaro Rico-Pérez, Manuel Javier Rosel-Solis, Luis Jesús Villarreal-Gómez, Yuridia Vega, José Omar Dávalos-Ramírez
المصدر: Applied Sciences, Vol 11, Iss 3, p 1350 (2021)
بيانات النشر: MDPI AG, 2021.
سنة النشر: 2021
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: inverse artificial neural network, finite element method, mini plate and screw, absorbable, optimization, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Mandibular fractures are common facial lesions typically treated with titanium plate and screw systems; nevertheless, this material is associated with secondary effects. Absorbable material for implants is an alternative to titanium, but there are also problems such as incomplete screw insertion and screw breakage due to high pretension in the screw caused by the insertion torque. The purpose of this paper is to find the optimal screw pretension (SP) in absorbable plate and screw systems by means of artificial neural network (ANN) and its inverse (ANNi). This optimal SP must satisfy a desired maximum von Mises strain (MVMS). For training the ANN, a database was generated by means of a design of experiments (DOE). Each DOE configuration was solved by means of finite element method (FEM) calculations. To obtain the optimal value for (SP) in the mini absorbable screw for fracture fixation, a strategy to invert the ANN is developed. Using the ANN coefficients, a sensitive study was performed to identify the influence of the design parameters in the MVMS. The optimal SP obtained was 14.9742 N. The MVMS condition was satisfied with an error less than 1.1% in comparison with FEM and ANN results. The screw shaft length is the most influencing MVMS parameter.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/11/3/1350; https://doaj.org/toc/2076-3417
DOI: 10.3390/app11031350
URL الوصول: https://doaj.org/article/7879d5f5558b4ee394b943b45f8db48b
رقم الأكسشن: edsdoj.7879d5f5558b4ee394b943b45f8db48b
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app11031350