Identification of NR and EPDM samples by means of thermogravimetric analysis and multivariate methods
Products based on ethylene-propylene-diene m-class (EPDM) and natural rubber (NR) are widely used in a different applications, including the automotive industry, heating, ventilation, and air conditioning applications, roofing systems, or the construction sector among others. The growing demand of t...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/97597 |
| Acceso en línea: | https://hdl.handle.net/2117/97597 https://dx.doi.org/10.1109/JSEN.2016.2603172 |
| Access Level: | acceso abierto |
| Palabra clave: | Polymers Vulcanization thermogravimetric analysis multivariate methods vulcanization classification identification Polímers Vulcanització Àrees temàtiques de la UPC::Enginyeria dels materials::Materials plàstics i polímers |
| Sumario: | Products based on ethylene-propylene-diene m-class (EPDM) and natural rubber (NR) are widely used in a different applications, including the automotive industry, heating, ventilation, and air conditioning applications, roofing systems, or the construction sector among others. The growing demand of these types of polymeric products has forced rubber industry to implement strict control schemes to ensure the specifications of the final products. The focus of this paper is the identification of different treatment types of NR and EPDM samples without any preceding analytical treatment to carry out a fast and reliable supervision of the vulcanization processes to improve the quality of the final rubber products. To this end, the thermogravimetric analysis (TGA) technique is applied in combination with the principal component analysis (PCA) and canonical variate analysis (CVA) multivariate feature extraction methods and the k-nearest neighbor (k-NN) classifier. Experimental results prove the suitability of the proposed approach and the potential of the TGA method for a fast supervision of the vulcanization processes. Using the information provided by the TGA technique in association with the PCA + CVA + k-NN approach, the system achieved 100% identification accuracy. |
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