A new hybrid LGPMBWM-PIV method for automotive material selection

• Main Authors: Wakeel, Saif, Bingol, Sedat, Ahmad, Shafi, Bashir, M. Nasir, Emamat, Mir Seyed Mohammad Mohsen, Ding, Zhou, Fayaz, H.
• Format: Article
• Published: Slovensko Drustvo Informatika 2021
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.um.edu.my/35514/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105479766&doi=10.31449%2finf.v45i1.3246&partnerID=40&md5=369d1c63d110ca1d5a084affe70f4d50

Efforts are continuously being made by researchers to improve fuel efficiency and to reduce CO2 emissions from the passenger cars. To achieve these goal, recent trend is to make the cars components light in weight for which manufacturing car roofs using natural fiber reinforced composites (NFCs) is one of the method. Several natural fibers (NFs)are available as alternative reinforcements for the fabrication of NFCs. Different NFs possess different properties and therefore, it is necessary to select the most appropriate natural fiber for fabrication of the composites which in turn will lead to the desired performance of the vehicle. Selection of the optimal natural fiber, amongst the several alternatives, is basically a multi criteria decision making (MCDM) problem as selection is based on the evaluation of several conflicting criteria. In this study, twelve alternative natural fibers (Flax, Hemp, Jute, Kenaf, Ramie, Okra, PALF, Coir, Isora, Cotton, Banana and Sisal) and six evaluation criteria (Tensile strength, Stiffness, Failure strain, Density, Degradation temperature and Moisture gain) are considered and selection of the optimal NF is made using a newly developed hybrid MCDM method i.e. Linear goal programming model for Best-Worst method (LGPMBWM) and Proximity index value method (PIV). Results of the study reveal that among all considered natural fibers, Ramie fiber is the most suitable alternative for the fabrication of composites and coir fiber is the worst candidate for the same. Ranking results were also supported by five other MCDM methods as there was a strong correlation between PIV and other MCDM methods. © 2021 Slovene Society Informatika. All rights reserved.