Accelerated Discovery of 3D Printing Materials Using Data-Driven Multi-Objective Optimization

TL;DR

This paper presents a machine learning-driven multi-objective optimization method that accelerates the discovery of high-performance 3D printing materials, reducing experimental efforts and expanding the performance space.

Contribution

It introduces an autonomous, data-driven approach combining optimization algorithms with fabrication to efficiently discover optimal additive manufacturing materials.

Findings

Discovered 12 optimal composite formulations autonomously.

Expanded the performance space 288 times after 30 experiments.

Reduced the number of experiments needed for material discovery.

Abstract

Additive manufacturing has become one of the forefront technologies in fabrication, enabling new products impossible to manufacture before. Although many materials exist for additive manufacturing, they typically suffer from performance trade-offs preventing them from replacing traditional manufacturing techniques. Current materials are designed with inefficient human-driven intuition-based methods, leaving them short of optimal solutions. We propose a machine learning approach to accelerate the discovery of additive manufacturing materials with optimal trade-offs in mechanical performance. A multi-objective optimization algorithm automatically guides the experimental design by proposing how to mix primary formulations to create better-performing materials. The algorithm is coupled with a semi-autonomous fabrication platform to significantly reduce the number of performed experiments and overall time to solution. Without any prior knowledge of the primary formulations, the proposed methodology autonomously uncovers twelve optimal composite formulations and enlarges the discovered performance space 288 times after only 30 experimental iterations. This methodology could easily be generalized to other material formulation problems and enable completely automated discovery of a wide variety of material designs.