Non-dimensionalization of variables to enhance machine learning in additive manufacturing processes

This invention describes a method for automatically controlling a 3D printing process. It involves taking various measurements from the printing process and converting them into universal, unit-less ratios. These ratios are used to train a machine learning model that predicts how the final part will turn out, such as its dimensions or surface smoothness. The system then uses these predictions to adjust the 3D printer's settings in real-time to achieve a specified physical property for the fabricated part.

Why it matters: Filed before widespread adoption of real-time machine learning in manufacturing. Advances in ML frameworks and computational power now make precise, closed-loop control for additive manufacturing more practical.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedMachine learning software, AM process integration

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