ArticleScientific reports2026
Development of ranking alternatives of micro-cup production from directionally rolled copper rods using the Intuitionistic Fuzzy MARCOS method.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
This study aims to develop a sustainable and efficient simulation-assisted micro-deep drawing framework by integrating the Intuitionistic Fuzzy MARCOS method for multi-criteria decision-making. The focus is on optimizing multiple conflicting performance parameters, including tool force, springback, formability, and thinning rate. An eight-stage micro-deep drawing model was developed and simulated through Finite Element Analysis (FEA) using directionally rolled and recrystallized copper sheets. The Intuitionistic Fuzzy MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method was then employed to estimate and rank different process parameter combinations, enabling the systematic selection of the most favorable conditions for high-quality micro-cup production. ANOVA and Goodness-of-Fit metrics were used to validate the model’s significance and accuracy. Key parameters—clearance, punch radius, draw ratio, and type of dry lubricant—were optimized and experimentally validated. The optimal configuration (Clearance: 0.285 mm, Punch Radius: 1.5 mm, Draw Ratio: 1.69, Graphite lubricant) outperformed others in terms of mechanical stability, dimensional accuracy, and formability. High R2, adjusted R2, and adequate precision indicated strong model reliability. Experimental validations, including microstructural analysis, surface roughness, hardness, and springback, confirmed the simulation predictions. The study is limited to a specific draw ratio range and material (recrystallized copper), which may affect its broader applicability. Future studies could incorporate more materials and real-time sensing. The approach minimizes trial-and-error, reduces material waste, and supports sustainable micro-manufacturing by improving efficiency and product quality. This research integrates fuzzy optimization with finite element modeling and experimental validation, providing a novel, accurate, and sustainable framework for micro-forming process optimization.
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