Fundamental Scaling Relationships in Additive Manufacturing and their Implications for Future Manufacturing Systems

TL;DR

This paper introduces a universal mathematical model for additive manufacturing, revealing fundamental scaling laws and limits, and discusses how process innovations could push beyond current boundaries.

Contribution

It presents a simplified model capturing AM process dynamics, identifies key scaling relationships, and proposes a new classification framework for manufacturing paradigms.

Findings

Inverse-cubic dependency of manufacturing time on minimal feature size

Linear dependency of manufacturing time on structure size

Varying voxel size can help overcome fundamental limits

Abstract

The field of additive manufacturing (AM) has advanced considerably over recent decades through the development of novel methods, materials, and systems. However, as the field approaches maturity, it is relevant to investigate the scaling frontiers and fundamental limits of AM in a generalized sense. Here we propose a simplified universal mathematical model that describes the essential process dynamics of many AM hardware platforms. We specifically examine the influence of several key parameters on total manufacturing time, comparing these with performance results obtained from real-world AM systems. We find a inverse-cubic dependency on minimal feature size and a linear dependency on overall structure size. These relationships imply how certain process features such as parallelization and process dimensionality can help move toward the fundamental limits. AM methods that are capable of varying the size of deposited voxels provide one possibility to overcome these limits in the future development of AM. We also propose a new framework for classifying manufacturing processes as "top-down" vs "bottom-up" paradigms, which differs from the conventional usage of such terms, and present considerations for how "bottom-up" manufacturing approaches may surpass the fundamental limits of "top-down" systems.