PSI - Issue 72

Ruhit Bardhan et al. / Procedia Structural Integrity 72 (2025) 507–519

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4.3. Comparison with Classical and Fuzzy TOPSIS

We contrasted the outcome of the suggested neutrosophic TOPSIS framework with those from the fuzzy and traditional TOPSIS method in order to assess its efficacy. For this comparison, the neutrosophic values were converted to crisp values for classical TOPSIS, and to triangular fuzzy numbers for fuzzy TOPSIS shows in Figure 1.

Figure 1. Comparison of closeness coefficients using different TOPSIS methods.

Table 5, Comparison of Rankings Using Different TOPSIS Methods

A A A A A

Alternative

Classical TOPSIS

Fuzzy TOPSIS

Neutro-sophic TOPSIS

1 2 3 4 5

4 2 1 5 3

4 2 1 5 3

3 2 1 5

4 ZrO 2 -NiCoCrAlY FGM ( 3 ) was the best option according to all three methodologies, although the ranks of the other options varied. The neutrosophic TOPSIS method showed higher discrimination power, as evidenced by the larger gaps between the closeness coefficients, especially between the top- ranked alternatives, shows in Table 5. Furthermore, a key advantage of the neutrosophic approach is its ability to capture indeterminacy separately from membership and non-membership, which is particularly valuable for FGM evaluation where uncertainty is inherent in gradient properties. 4.4. Thermal Gradient Performance Visualization To further analyze the performance of the top-ranked FGM alternatives, we visualized their thermal gradient properties. Figure 2 shows the temperature distribution and thermal stress profiles for the top three FGM alternatives under typical operating conditions. These visualizations reveal that the ZrO 2 -NiCoCrAlY FGM ( 3 ) exhibits superior thermal gradient management, with a more gradual temperature transition and lower peak thermal stresses compared to the other alternatives, shows Figure 3. This confirms the ranking obtained through the neutrosophic TOPSIS analysis and highlights the practical significance of the results.