A Neuroevolutionary Model to Estimate the Tensile Strength of Manufactured Parts Made by 3D Printing

作     者：da Silva, Matheus Alencar Junior, Bonfim Amaro Brito Medeiros, Ramon Ruda Pinheiro, Placido Rogerio 

作者机构：Univ Fed Ceara Undergrad Mech Engn Program BR-62900000 Russas Brazil Univ Fed Ceara Nucleo Estudo Machine Learning & Otimizacao NEMO BR-62900000 Russas Brazil Fed Univ Paraiba UFPB Grad Program Mech Engn BR-58051900 Joao Pessoa Paraiba Brazil Univ Fortaleza UNIFOR Grad Program Appl Informat BR-60811905 Fortaleza Ceara Brazil 

出 版 物：《ALGORITHMS》 (算法)

年 卷 期：2022年第15卷第8期

页      面：263-263页

核心收录：

学科分类：0301[法学-法学] 03[法学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：fused deposition modeling 3D printing artificial intelligence 

摘      要：Three-dimensional printing has advantages, such as an excellent flexibility in producing parts from the digital model, enabling the fabrication of different geometries that are both simple or complex, using low-cost materials and generating little residue. Many technologies have gained space, highlighting the artificial intelligence (AI), which has several applications in different areas of knowledge and can be defined as any technology that allows a system to demonstrate human intelligence. In this context, machine learning uses artificial intelligence to develop computational techniques, aiming to build knowledge automatically. This system is responsible for making decisions based on experiences accumulated through successful solutions. Thus, this work aims to develop a neuroevolutionary model using artificial intelligence techniques, specifically neural networks and genetic algorithms, to predict the tensile strength in materials manufactured by fused filament fabrication (FFF)-type 3D printing. We consider the collection and construction of a database on three-dimensional instances to reach our objective. To train our model, we adopted some parameters. The model algorithm was developed in the Python programming language. After analyzing the data and graphics generated by the execution of the tests, we present that the model outperformed, with a determination coefficient superior to 90%, resulting in a high rate of assertiveness.