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Additive manufacturing has become one of the forefront technologies infabrication, enabling new products impossible to manufacture before. Althoughmany materials exist for additive manufacturing, they typically suffer fromperformance trade-offs preventing them from replacing traditional manufacturingtechniques. Current materials are designed with inefficient human-drivenintuition-based methods, leaving them short of optimal solutions. We propose amachine learning approach to accelerate the discovery of additive manufacturingmaterials with optimal trade-offs in mechanical performance. A multi-objectiveoptimization algorithm automatically guides the experimental design byproposing how to mix primary formulations to create better-performingmaterials. The algorithm is coupled with a semi-autonomous fabrication platformto significantly reduce the number of performed experiments and overall time tosolution. Without any prior knowledge of the primary formulations, the proposedmethodology autonomously uncovers twelve optimal composite formulations andenlarges the discovered performance space 288 times after only 30 experimentaliterations. This methodology could easily be generalized to other materialformulation problems and enable completely automated discovery of a widevariety of material designs.

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