New machine learning framework enhances precision and efficiency in metal 3D printing, advancing sustainable manufacturing
Peer-Reviewed Publication
Updates every hour. Last Updated: 18-Jul-2025 11:11 ET (18-Jul-2025 15:11 GMT/UTC)
Researchers at University of Toronto Engineering, led by Professor Yu Zou, are leveraging machine learning to improve additive manufacturing, also commonly known as 3D printing. In a new paper, published in the journal of Additive Manufacturing, the team introduces a new framework they’ve dubbed the Accurate Inverse process optimization framework in laser Directed Energy Deposition (AIDED).
The new AIDED framework optimizes laser 3D printing to enhance the accuracy and robustness of the finished product. This advancement aims to produce higher quality metal parts for industries, such as aerospace, automotive, nuclear and health care, by predicting how the metal will melt and solidify to find optimal printing conditions.
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