A liquid robot capable of transforming, separating, and fusing freely like living cells has been developed. Seoul National University College of Engineering announced that a joint research team led by Professor Ho-Young Kim from the Department of Mechanical Engineering, Professor Jeong-Yun Sun from the Department of Materials Science and Engineering, and Professor Keunhwan Park from the Department of Mechanical, Smart, and Industrial Engineering at Gachon University has successfully developed a next-generation soft robot based on liquid. This research was published in the world-renowned journal Science Advances on March 21.

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.