Sustainable additive manufacturing with FDM: Taguchi-based parameter optimization and AI-driven prediction of mechanical and environmental metrics

Additive manufacturing (AM), particularly fused deposition modeling (FDM), has emerged as a transformative technique for producing customized, lightweight, and environmentally sustainable components. However, identifying optimal process parameters that balance mechanical performance with environmental impact remains a critical challenge. In this study, a comprehensive experimental and data-driven approach is employed to investigate the influence of key FDM parameters—infill density (ID) (30%, 60%, 90%), layer thickness (LT) (200, 300, 400 µm), printing speed (PS) (50, 100, 150 mm/s), and infill pattern (IP) (Cubic, Triangles, Concentric)—on the mechanical and sustainability-related performance of acrylonitrile…