Investigating Novel Parameters for Enhanced Environmental Sustainability in 3D Printing

Abstract

Additive Manufacturing (AM) widely known as 3D printing has revolutionized how we design and produce components, especially those with complex geometries and tailored properties. While standard process settings like layer thickness, infill density, and print speed are well established, today's growing demand for high-performance, multi-material parts with integrated functions is pushing the boundaries of what's possible. This paper delves into lesser-explored and emerging parameters that significantly influence the mechanical, thermal, electrical, and aesthetic qualities of 3D-printed parts. We examine how next-generation control algorithms, material-specific variables, and the integration of artificial intelligence (AI) and machine learning (ML) are shaping the future of parameter optimization in AM. The goal is to provide a comprehensive understanding of how these novel factors interact and how they can unlock new capabilities and applications in 3D printing for smarter, stronger, and more functional products.