首页|Researchers at University of Sheffield Have Published New Study Findings on Robo tics and Artificial Intelligence (Remotely actuated programmable self-folding or igami strings using magnetic induction heating)

Researchers at University of Sheffield Have Published New Study Findings on Robo tics and Artificial Intelligence (Remotely actuated programmable self-folding or igami strings using magnetic induction heating)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s and artificial intelligence. According to news originating from Sheffield, Uni ted Kingdom, by NewsRx correspondents, research stated, “Transforming planar str uctures into volumetric objects typically requires manual folding processes, aki n to origami.” Our news editors obtained a quote from the research from University of Sheffield : “However, manual intervention at sub-centimeter scales is impractical. Instead , folding is achieved using volume-changing smart materials that respond to phys ical or chemical stimuli, be it with direct contact such as hydration, pH, or re motely e.g., light or magnetism. The complexity of small-scale structures often restricts the variety of smart materials used and the number of folding sequence s. In this study, we propose a method to sequentially self-fold millimeter scale origami using magnetic induction heating at 150kHz and 3.2 mT. Additionally, we introduce a method for designing self-folding overhand knots and predicting the folding sequence using the magneto-thermal model we developed. This methodology is demonstrated to sequentially self-fold by optimizing the surface, placement, and geometry of metal workpieces, and is validated through the self-folding of various structures, including a 380 mm2 croissant, a 321mm2 box, a 447mm2 bio-mi metic Mimosa pudica leaf, and an overhand knot covering 524mm2.”

University of SheffieldSheffieldUnit ed KingdomEuropeMachine LearningRobotics and Artificial Intelligence

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Sep.18)