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ARTICLE
Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach
1 Department of Mechanical and Manufacturing, Universiti Putra Malaysia, Selangor, Malaysia
2 Department of Manufacturing Engineering Technology, Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
* Corresponding Author: S. M. Sapuan. Email:
(This article belongs to the Special Issue: Natural Fibre Composites: Design, Materials Selection and Fabrication)
Journal of Renewable Materials 2023, 11(5), 2381-2397. https://doi.org/10.32604/jrm.2023.026549
Received 12 September 2022; Accepted 03 November 2022; Issue published 13 February 2023
Abstract
Natural fibre reinforced polymer composite (NFRPC) materials are gaining popularity in the modern world due to their eco-friendliness, lightweight nature, life-cycle superiority, biodegradability, low cost, and noble mechanical properties. Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification, material selection has become a crucial component of design for engineers. This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC, and GRA approaches. The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches: qualitative and quantitative. This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σ technique, with the decision based on the highest performance, the lightest weight, and the most environmentally friendly criteria. The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase. In the future study, selection technique may have been more exhaustive if more information from other factors had been added.Keywords
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