Application of an IEW-CRITIC-CoCoSo method based on interval-valued T-spherical fuzzy for optimizing process parameters of 3D printed recycled polypropylene composites.

With the rapid development of sustainable materials and additive manufacturing technologies, glass fiber-reinforced recycled polypropylene (GF/RPP) has shown enormous potential for application in fused deposition modeling (FDM). However, the mechanical performance of GF/RPP parts is significantly affected by the FDM process parameters, and multi-objective parameter optimization remains a critical challenge. To address this, a novel multi-attribute decision-making (MADM) framework based on the interval-valued T-spherical fuzzy weighted power Heronian mean operator and Combined Compromise Solution (IVTSFWPHM-CoCoSo) is proposed to optimize FDM process parameters. The framework employs the IVTSFWPHM operator to handle experimental uncertainty, capture interactions among multiple mechanical properties, and reduce the influence of extreme values. The improved entropy weight and criteria importance through intercriteria correlation (IEW-CRITIC) method are used to determine weights. Finally, CoCoSo is applied to reliably rank the parameter combinations. The results show that a printing temperature of 240 °C, a layer thickness of 0.3 mm, and an infill density of 60% achieve the best overall mechanical properties across different raster angles, improving performance by approximately 10.7%. The effectiveness of the proposed method is further validated through comparison with existing methods and scanning electron microscopy analysis. This study provides a practical reference for complex decision-making in the FDM printing of recycled composites.