Neutrosophic method to evaluate a new polypropylene and hemp composite material for the replacement of current door materials in vehicles

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Vinicio Sebastián Ribadeneira Ramírez
Juan Diego Zurita Vargas
Antonio Gabriel Castillo Medina

Abstract

The selection of new materials to replace the current ones used in vehicle doors is essential to improve energy efficiency, reduce costs, and minimize environmental impact. In this context, the objective of this research is to implement a neutrosophic method to evaluate the most suitable materials for this application. The neutrosophic methodology, which is based on neutrosophic set theory, addresses uncertainty and imprecision in material evaluation. This approach will facilitate the integration of various qualitative and quantitative variables, such as cost, mechanical strength, durability, and environmental impact, allowing for a more complete and realistic ranking of the selected options. Candidate materials include ferrous and non-ferrous metal alloys, thermoplastic polymers, and composites, which have been shown to be lighter and, in many cases, superior in performance compared to YS170 steel. By assigning neutrosophic evaluation functions to these materials, a multidimensional analysis will be achieved, taking into account specific design preferences and constraints.


At the end of the process, this approach will identify an optimized set of materials that meet the design and functional requirements, thus contributing to the sustainable evolution of the automotive industry and the development of more efficient and safer vehicle doors.

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Neutrosophic method to evaluate a new polypropylene and hemp composite material for the replacement of current door materials in vehicles. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 424-433. http://fs.unm.edu/NCML2/index.php/112/article/view/738
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How to Cite

Neutrosophic method to evaluate a new polypropylene and hemp composite material for the replacement of current door materials in vehicles. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 424-433. http://fs.unm.edu/NCML2/index.php/112/article/view/738