ArticleScientific reports2026
Development of fuzzy-based ranking alternatives by hybrid RSM-fuzzy AHP-fuzzy CoCoSo framework sustainable ultrasonic-assisted turning of magnesium alloys.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Hybrid RSM-SF-AHP-fuzzy MARCOS approach for multi-response optimization of WAAM-fabricated Ni-SS bimetallics in face milling.Scientific reports · 2026Article
- A hybrid RSM-Spherical Fuzzy AHP-Fuzzy VIKOR approach for optimizing drilling of AM60 magnesium alloy using biodegradable MQL.Scientific reports · 2026Article
- Multi-criteria wear performance optimization of waste-reinforced hybrid aluminum alloys via RSM, Fuzzy AHP, and Fuzzy WASPAS.Scientific reports · 2026Article
- A novel sustainable hybrid intuitionistic fuzzy decision-making model for machinability ranking of Al-Cu-Mg-SiC-graphite-peanut shell hybrid composites.Scientific reports · 2026Article
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4 authors.
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Abstract
This study aims to optimize the machining performance of magnesium alloys fabricated by stir casting in ultrasonic-assisted turning (UAT) by enhancing surface quality, extending tool life, and increasing material removal rate (MMR), contributing to sustainable manufacturing practices. A hybrid optimization approach combining Response Surface Methodology (RSM) and Fuzzy CoCoSo was employed. The RSM design, based on the Hybrid Box-Behnken model, was used to select experimental parameters, while Fuzzy AHP determined fuzzy criteria weights. The optimal process parameters were identified by Fuzzy CoCoSo. The study focused on cutting speed (11.00 to 42.00 m/min), feed rate (0.0500 to 0.1500 mm/rev), and ultrasonic power intensity (0.0000 to 100.00%). A total of 15 runs in a quadratic design were conducted to assess surface roughness, tool wear, MMR, energy consumption, and dimensional accuracy. Confirmation experiments validated the model's predictions. The study revealed that ultrasonic parameters, particularly frequency and amplitude, significantly influenced machining performance. The optimal settings of 26.5 m/min cutting speed, 0.15 mm/rev feed rate, and 100% ultrasonic power intensity resulted in improved surface roughness (0.72 μm), enhanced MMR (0.95 cm³/min), reduced tool wear (60 μm), lower energy consumption (160 W), and better dimensional accuracy (97%). Although the study provides valuable insights into UAT for stir-cast magnesium alloys, further investigations are needed to evaluate the long-term effects of optimized parameters and their applicability to different alloy compositions. The optimized parameters can be directly applied in industrial machining of stir-cast magnesium alloys, offering both cost-efficiency and sustainability benefits, including reduced energy consumption, improved surface quality, and longer tool life. This research introduces a novel hybrid optimization framework integrating RSM, Fuzzy AHP, and Fuzzy CoCoSo to optimize UAT for stir-cast magnesium alloys, advancing sustainable machining technologies.
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