Data-driven wear performance optimization of WAAM aluminum 5356 via intuitionistic fuzzy decision modeling

This study aims to enhance the tribological performance of Wire Arc Additive Manufacturing (WAAM)-fabricated Aluminum 5356 by optimizing key operating parameters using an Intuitionistic Fuzzy-based Multi-Criteria Decision-Making (MCDM) framework. The objective was to minimize wear-related degradation and frictional losses under severe loading conditions relevant to high-performance engineering applications. Tribological experiments were conducted by varying load (400-800 N), rotational speed (2-6 rpm), and sliding distance (20-60 m) under different lubrication regimes. The measured responses included wear rate, coefficient of friction, mass loss, frictional force, wear scar diameter, and temperature rise. ANOVA and R² analysis were employed to validate the experimental trends, while Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) was used to rank the alternatives and identify the optimum condition; ranking robustness was further examined through sensitivity analysis. The results revealed that the most favourable tribological condition was obtained at a load of 400 N, speed of 6 rpm, and sliding distance of 20 m under solid lubrication, yielding the lowest wear rate (0.0535 mm/h) and coefficient of friction (0.3614). Compared with grease lubrication under the same operating parameters, the optimized condition reduced wear rate by 6.14%, coefficient of friction by 4.87%, wear scar diameter by 14.01%, and frictional force by 7.06%, confirming the superior protective action of the solid lubricant film. The findings demonstrate that the proposed IF-TOPSIS framework provides a robust and systematic route for tribological optimization of WAAM-fabricated Aluminum alloys. However, the conclusions are specific to WAAM-fabricated Aluminum 5356 under controlled laboratory conditions and should not be generalized to other alloy systems without further validation.