ArticleScientific reports2026
Development of a novel integrated RSM-Fuzzy MCDA framework for high-speed machinability assessment of WAAM-Fabricated aluminium.
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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Who cites it
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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Authors and funding
3 authors.
Funding
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Abstract
This study aims to optimize the high-speed machining process of WAAM-fabricated aluminum, focusing on enhancing machinability indicators while aligning with sustainable manufacturing practices. A novel hybrid optimization framework combining Response Surface Methodology (RSM), Fuzzy Logarithmic Methodology of Additive Weights (LMAW), and Fuzzy WASPAS was developed to optimize parameters such as surface finish, material removal rate, cutting force, and power consumption. The optimization model identified optimal machining parameters (8000 RPM cutting speed, 0.1 mm/tooth feed rate, 1 mm depth of cut, and a four-flute tool), achieving a surface roughness (Ra) of 0.35 microns, material removal rate (MRR) of 38.4 mm³/min, cutting force (Fc) of 114 N, and power consumption (Pc) of 312 W. Validation tests confirmed the accuracy of the predicted results. The study focuses on specific machining conditions and tool configurations. Future research could explore different tool materials and machining environments to further enhance the model’s applicability. The findings provide insights that can help industry practitioners optimize machining processes, improve efficiency, and contribute to sustainable manufacturing aligned with SDGs. This research introduces a novel hybrid optimization methodology combining RSM, Fuzzy LMAW, and Fuzzy WASPAS, contributing to both scientific understanding and industrial practices in sustainable machining.
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