Evaluation of drone propeller materials through geometrics components using multi criteria decision making methods
Year: 2026
Type: article
Abstract: Drones are widely used many different industries like Defence, Agriculture, Entertainment, Infrastructures inspections and monitoring of environmental etc. In these applications Drone’s lift the propulsion, stability, maneuverability, and overall efficiency is great depends on its drone propeller. The choice of material for the drone propellers significantly affects the performance, durability, and efficiency. This study assess the important criteria while choosing the propeller materials such as stiffness, strength, durability, cost effectiveness, manufacturability, sustainability, density, and strength- to- weight ratio. The study presented and applied different multi criteria decision making (MCDM) techniques are MEREC, CRITIC, FUCOM, SWARA, and Entropy to evaluate these criteria. Further the study expands it evaluation to determine optimal materials for drone propellers using geometric based MCDM methods which are RAPS, RAMS, RATMI, and MCRAT. The results indicate that while each method has its owen strengths and weaknesses, a composite approach incorporating relative importance of the properties materials proves to the most effective. This comprehensive analysis offers valuable insights for selecting the most suitable material for enhancing drone performance across various applications. This study also examined the correlation among four multi criteria assessment techniques like RAPS, RAMS, MCRAT and RATMI. The relationships between variable were assessed using Pearson’s correlation coefficient values, Kendall’s Tau and Rank-Biased Overlap (RBO). Strong positive correlations were found among MCRAT, RATMI and RAPS, while RAMS displayed notable divergence. These results are suggested that, even though general methodologies are interchangeable, their subtle differences should be considered for specific research applications.
Institution: Andhra University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7119527288
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