Aplicação do TOPSIS na análise do processo de infusão a vácuo para fabricação de compósitos com fios de juta
Application of TOPSIS in evaluating the vacuum infusion process toward manufacturing of jute yarn composites
Alessandro de Castro Corrêa, Jean da Silva Rodrigues, Tainã Fernandes Rodrigues, Carlos André Corrêa de Mattos, Danielle Cristina Gonzaga Corrêa, Cláudia Canto de Souza Leão
Resumo
O objetivo deste estudo foi analisar o desempenho do processo de infusão a vácuo (VIP) na fabricação de compósitos de fios de juta e resina poliéster em comparação ao processo de laminação manual (hand lay-up), considerando múltiplos atributos com apoio do Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). O experimento envolveu a produção de compósitos de matriz de poliéster insaturado reforçados por fios de juta na forma de tecido plano. As medidas de desempenho foram a porosidade, resistência à tração, custos, redução de emissão da voláteis e a expectativa de contribuição social. Os pesos dos atributos foram determinados objetivamente a partir da informação disponível em cada critério na matriz de decisão com base na entropia da informação. O processo de laminação manual foi utilizado como referência de avaliação por ser considerado o processo convencional. Os resultados revelaram que os atributos com maior peso foram a redução de emissão de voláteis e a porosidade; que o processo VIP apresentou um desempenho superior ao processo hand lay-up; e que a TOPSIS foi apropriada a análise do problema.
Palavras-chave
Abstract
This study aims to compare the vacuum infusion process (VIP) to the conventional hand lay-up process (HLU) applied to the manufacturing of jute yarn and polyester resin composites, considering multiple attributes with the assistance of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The experiment consisted of producing unsaturated polyester composites reinforced by jute yarn, arranged in the form of fabric in plan style. The attributes considered were porosity, tensile strength, costs, reduction of volatile emissions, and social perspective. Shannon’s entropy was applied to determine attributes weights objectively. HLU was selected as the benchmark because it is considered the conventional process. The results obtained show that reduction of volatile emissions and tensile strength were the weightiest attributes, the VIP was the best choice, and TOPSIS was appropriate for the problem analysis.
Keywords
Referências
1 Ashby M. Seleção de materiais no projeto mecânico. 4 ed. Rio de Janeiro: Elsevier; 1996.
2 Levy F No, Pardini LC. Compósitos estruturais: ciência e tecnologia. São Paulo: Edgar Blucher; 2006.
3 Ragondet A. Experimental characterization of the vacuum infusion process [thesis]. Nottingham: University of Nottingham; 2005.
4 Hwang CL, Yoon K. Multiple attribute decision making: methods and applications. New York: Springer-Verlag; 1981.
5 Jahan A, Edwards KL, Bahraminasab M. Multi-criteria decision analysis for supporting the selection of engineering materials in product design. Oxford: Butterworth-Heinemann; 2016.
6 Milani AS, Shanian A, Madoliat R, Nemes JA. The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Structural and Multidisciplinary Optimization. 2005;29(4):312-318.
7 Shanian A, Savadogo O. TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of Power Sources. 2006;159(2):1095-1104.
8 Govindan K, Madan Shankar K, Kannan D. Sustainable material selection for construction industry: a hybrid multi criteria decision making approach. Renewable & Sustainable Energy Reviews. 2016;55:1274-1288.
9 Shanian A, Savadogo O. A methodological concept for material selection of highly sensitive components based on multiple criteria decision analysis. Expert Systems with Applications. 2009;36(2):1362-1370.
10 Chauhan A, Vaish R. Magnetic material selection using multiple attribute decision making approach. Materials & Design. 2012;36:1-5.
11 Chauhan A, Vaish R. A comparative study on decision making methods with interval data. Journal of Computational Engineering. 2014;2014:1-10.
12 Karande P, Chakraborty S. Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design. 2012;37:317-324.
13 Çalışkan H, Kurşuncu B, Kurbanoğlu C, Güven ŞY. Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods. Materials & Design. 2013;45:473-479.
14 Anojkumar L, Ilangkumaran M, Sasirekha V. Comparative analysis of MCDM methods for pipe material selection in sugar industry. Expert Systems with Applications. 2014;41(6):2964-2980.
15 Mansor MR, Sapuan SM, Hambali A, Zainudin ES, Nuraini AA. Materials selection of hybrid bio-composites thermoset matrix for automotive bumper beam application using TOPSIS method. Advances in Environmental Biology. 2014;(8):3138-3142.
16 Mansor MR, Sapuan SM, Zainudin ES, Nuraini AA, Hambali A. Application of integrated AHP-TOPSIS method in hybrid natural fiber composites materials selection for automotive parking brake lever component. Australian Journal of Basic and Applied Sciences. 2014;8(5):431-439.
17 Al-Oqla FM, Sapuan SM, Ishak MR, Nuraini AA. Decision making model for optimal reinforcement condition of natural fiber composites. Fibers and Polymers. 2015;16(1):153-163.
18 Saaty TL. The analytic hierarchy process: planning, priority, setting and resource allocation. New York: McGrawHill; 1980.
19 Shannon CE, Weaver W. A mathematical theory of communication. The Bell System Technical Journal. 1948;27(3):379-423.
20 Rodrigues J, Souza JA, Fujiyama R. Compósitos poliméricos reforçados com fibras naturais da Amazônia fabricados por infusão. Revista Matéria. 2015;20(4):946-960.
21 American Standard Test Methods. ASTM D3039: standard test method for tensile properties of polymer matrix composite materials. West Conshochen: ASTM; 2008.
22 R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2018 [acesso em 28 maio 2019]. Disponível em: https://www.R-project.org/
23 R Core Team. CRAN: the comprehensive archive network. Vienna: R Foundation for Statistical Computing; 2018 [acesso em 28 maio 2019]. Disponível em: https://www.r-project.org/
24 Franco RAVS. Produção de componentes em materiais compósitos por infusão de resina [dissertação]. Lisboa: Universidade Técnica de Lisboa; 2008.
Submetido em:
28/05/2019
Aceito em:
15/05/2020
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email