Parasitic weeds are ruthless freeloaders, stealing nutrients from crops and devastating harvests. But what if farmers could trick these invaders into self-destructing? Scientists at UC Riverside think they’ve found a way.

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.