dor_id: 4129371

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1101/895

100.1.#.a: sonawane, shriram shaligram

524.#.#.a: sonawane, shriram shaligram (2022). Artificial neural network model for prediction of viscoelastic behaviour of polycarbonate composites: viscoelastic behaviour of polycarbonate composites. Journal of Applied Research and Technology; Vol. 20 Núm. 2, 2022; 188-202. Recuperado de https://repositorio.unam.mx/contenidos/4129371

245.1.0.a: Artificial neural network model for prediction of viscoelastic behaviour of polycarbonate composites: viscoelastic behaviour of polycarbonate composites

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2022

264.#.1.c: 2022-05-02

653.#.#.a: Artificial neural network (ANN); Polycarbonate composites; Dynamic mechanical analyzer; Glass transition temperature; Storage modulus

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/1101

001.#.#.#: 074.oai:ojs2.localhost:article/1101

041.#.7.h: eng

520.3.#.a: Polymer composites are the result of incorporation of nanoparticles into the polymers and can lead to improvements even with a very small amount of reinforcement which can be tuned according to the applications. In order to understand the behaviour of these polymer composites we need to perform a number of characterizations and analyses which in turn requires investment of money and time. Thus, to reduce the number of characterizations and analyses for developing polymer composites, computational techniques can prove helpful. By means of a computational technique known as artificial neural network (ANN), prediction of the thermo-mechanical properties was made possible. Here dynamic mechanical analysis (DMA) data set was used for characterization of polycarbonate / calcium carbonate-SiO2 core shell composites (polycarbonate composites). Using the dataset, the selected ANN model consisted of a network of [3-10-1]. The prediction accuracy achieved using ANN method, was around 90%. Applicability and performance of ANN to the existing system was also confirmed by mean squared error (MSE), which is favourably small for this case, in the range of 10-5. The output predicted by ANN had a coefficient of correlation of 0.999. Furthermore, sensitivity analysis confirmed the importance of various input variables in relation with output. An optimization of the variables facilitated to maximize the conditions thus predicting glass transition temperature.

773.1.#.t: Journal of Applied Research and Technology; Vol. 20 Núm. 2 (2022); 188-202

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 188-202

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2022.20.2.1101

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

file_creation_date: 2022-04-21 19:28:35.0

file_modification_date: 2022-04-21 19:28:35.0

file_creator: Yolanda G.G.

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last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

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