Results 171 to 180 of about 63,920 (326)

Enabling Metal‐Based Soft Robotics Through Investment Casting

open access: yesAdvanced Intelligent Systems, EarlyView.
Vacuum investment casting enables manufacturing of compliant soft robotic structures out of AA7075 high‐strength aluminum alloy. Additively manufactured patterns are converted into metal soft robotic structures addressing long lasting challenges like durability and nonlinearity of elastomer‐based soft robotics.
Felix Pancheri, Tim C. Lueth, Yilun Sun
wiley   +1 more source

Predicting and Rationalizing Piezoelectricity in Racemic Bioorganic Molecular Crystals

open access: yesAngewandte Chemie, EarlyView.
Racemic molecular crystals, composed of equal mixtures of chiral enantiomeric components, are predicted to exhibit significant piezoelectric responses and favourable mechanical flexibility. This study identifies organic and bioorganic racemates as promising candidates for sustainable, lead‐free materials, enabling next‐generation applications in energy
Shubham Vishnoi, Sarah Guerin
wiley   +2 more sources

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

open access: yesAdvanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi   +3 more
wiley   +1 more source

Nonlinear resonant ultrasound spectroscopy for profiling thermal damage gradients and monitoring post-fire recovery in concrete

open access: hybrid
Massina Fengal   +6 more
openalex   +1 more source

Quantitative Phase-Space Nonlinear Ultrasound (PSNU) [PDF]

open access: yesASNT 27th Annual Research Symposium Proceedings, 2018
openaire   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

open access: yesAdvanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi   +5 more
wiley   +1 more source

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