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<p>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&#xa0;N), rotational speed (2–6&#xa0;rpm), and sliding distance (20–60&#xa0;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 R<sup>2</sup> 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&#xa0;N, speed of 6&#xa0;rpm, and sliding distance of 20&#xa0;m under solid lubrication, yielding the lowest wear rate (0.0535&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月5日

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  • S. P. Sundar Singh Sivam,
  • Adebayo Samuel Olabisi,
  • Stalin Kesavan

摘要

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.