{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:35:01Z","timestamp":1770917701217,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a15080263","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T20:49:28Z","timestamp":1659041368000},"page":"263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Neuroevolutionary Model to Estimate the Tensile Strength of Manufactured Parts Made by 3D Printing"],"prefix":"10.3390","volume":"15","author":[{"given":"Matheus Alencar da","family":"Silva","sequence":"first","affiliation":[{"name":"Undergraduate Mechanical Engineering Program, Federal University of Cear\u00e1, Russas 62900-000, Brazil"}]},{"given":"Bonfim","family":"Amaro Junior","sequence":"additional","affiliation":[{"name":"N\u00facleo de Estudo em Machine Learning e Otimiza\u00e7\u00e3o (NEMO), Federal University of Cear\u00e1, Russas 62900-000, Brazil"}]},{"given":"Ramon Rud\u00e1 Brito","family":"Medeiros","sequence":"additional","affiliation":[{"name":"Undergraduate Mechanical Engineering Program, Federal University of Cear\u00e1, Russas 62900-000, Brazil"},{"name":"Graduate Program in Mechanical Engineering, Federal University of Para\u00edba (UFPB), Jo\u00e3o Pessoa 58051-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1718-1712","authenticated-orcid":false,"given":"Pl\u00e1cido Rog\u00e9rio","family":"Pinheiro","sequence":"additional","affiliation":[{"name":"Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza 60811-905, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/17452759.2015.1097054","article-title":"3D printing of smart materials: A review on recent progresses in 4D printing","volume":"10","author":"Khoo","year":"2015","journal-title":"Virtual Phys. 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