Soft octopus-inspired suction cups using dielectric elastomer actuators with sensing capabilities.

Bioinspired and biomimetic soft grippers are rapidly growing fields. They represent an advancement in soft robotics as they emulate the adaptability and flexibility of biological end effectors. A prominent example of a gripping mechanism found in nature is the octopus tentacle, enabling the animal to attach to rough and irregular surfaces. Inspired by the structure and morphology of the tentacles, this study introduces a novel design, fabrication, and characterization method of dielectric elastomer suction cups. To grasp objects, the developed suction cups perform out-of-plane deflections as the suction mechanism. Their attachment mechanism resembles that of their biological counterparts, as they do not require a pre-stretch over a rigid frame or any external hydraulic or pneumatic support to form and hold the dome structure of the suction cups. The realized artificial suction cups demonstrate the capability of generating a negative pressure up to 1.3 kPa in air and grasping and lifting objects with a maximum 58 g weight under an actuation voltage of 6 kV. They also have sensing capabilities to determine whether the grasping was successful without the need of lifting the objects.

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks.

There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database retrieved from the open literature consisting of 810 test observations and 15 input features. A novel approach based on tabular generative adversarial networks was used to generate 6513 plausible synthetic data for training robust machine learning models, including random forest, extra trees, and gradient boosting regression. While the models were trained using the synthetic data, their ability to generalize their predictions was tested on the 810 experimental data thus far unknown and never presented to the models. The results indicate that the developed models achieved outstanding predictive performance. Parametric studies using the models were able to provide insight into the strength development mechanisms of UHPC and the significance of the various influential parameters.