Integrated manufacturing of REciclable multi-material COmposites for the TRANSport sector
Performance Optimization of Lignocellulosic Fiber-Reinforced Brake Friction Composite Materials Using an Integrated CRITIC-CODAS-Based Decision-Making Approach
A hybrid multicriteria decision-making (MCDM) framework, namely “criteria importance through inter-criteria correlation-combinative distance-based assessment” (CRITIC-CODAS) is introduced to rank automotive brake friction composite materials based on their physical and tribological properties. The ranking analysis was performed on ten brake friction composite material alternatives that contained varying proportions (5% and 10% by weight) of hemp, ramie, pineapple, banana, and Kevlar fibers. The properties of alternatives such as density, porosity, compressibility, friction coefficient, fade-recovery performance, friction fluctuation, cost, and carbon footprint were used as selection criteria. An increase in natural fiber content resulted in a decrease in density, along with an increase in porosity and compressibility. The composite with 5 wt.% Kevlar fiber showed the highest coefficient of friction, while the 5 wt.% ramie fiber-based composites exhibited the lowest levels of fade and friction fluctuations. The wear performance was highest in the composite containing 10 wt.% Kevlar fiber, while the composite with 10 wt.% ramie fiber exhibited the highest recovery. The results indicate that including different fibers in varying amounts can affect the evaluated performance criteria. A hybrid CRITIC-CODAS decision-making technique was used to select the optimal brake friction composite. The findings of this approach revealed that adding 10 wt.% banana fiber to the brake friction composite can give the optimal combination of evaluated properties. A sensitivity analysis was performed on several weight exchange scenarios to see the stability of the ranking results. Using Spearman’s correlation with the ranking outcomes from other MCDM techniques, the suggested decision-making framework was further verified, demonstrating its effectiveness and stability.
» Author: Tej Singh
» Reference: doi: 10.3390/su15118880
» Publication Date: 31/05/2023
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737
