Thermal Characterization of New 3D-Printed Bendable, Coplanar Capacitive Sensors

The Role of 3D Printing Technologies in Soft Grippers

Soft grippers are essential for precise and gentle handling of delicate, fragile, and easy-to-break objects, such as glassware, electronic components, food items, and biological samples, without causing any damage or deformation. This is especially important in industries such as healthcare, manufacturing, agriculture, food handling, and biomedical, where accuracy, safety, and preservation of the objects being handled are critical. This article reviews the use of 3D printing technologies in soft grippers, including those made of functional materials, nonfunctional materials, and those with sensors. 3D printing processes that can be used to fabricate each class of soft grippers are discussed. Available 3D printing technologies that are often used in soft grippers are primarily extrusion-based printing (fused deposition modeling and direct ink writing), jet-based printing (polymer jet), and immersion printing (stereolithography and digital light processing). The materials selected for fabricating soft grippers include thermoplastic polymers, UV-curable polymers, polymer gels, soft conductive composites, and hydrogels. It is conclude that 3D printing technologies revolutionize the way soft grippers are being fabricated, expanding their application domains and reducing the difficulties in customization, fabrication, and production.

RF. Comparison of the Capacitance of a Cyclically Fatigued Stretch Sensor to a Non-Fatigued Stretch Sensor When Performing Static and Dynamic Foot-Ankle Motions

Motion capture is the current gold standard for assessing movement of the human body, but laboratory settings do not always mimic the natural terrains and movements encountered by humans. To overcome such limitations, a smart sock that is equipped with stretch sensors is being developed to record movement data outside of the laboratory. For the smart sock stretch sensors to provide valuable feedback, the sensors should have durability of both materials and signal. To test the durability of the stretch sensors, the sensors were exposed to high-cycle fatigue testing with simultaneous capture of the capacitance. Following randomization, either the fatigued sensor or an unfatigued sensor was placed in the plantarflexion position on the smart sock, and participants were asked to complete the following static movements: dorsiflexion, inversion, eversion, and plantarflexion. Participants were then asked to complete gait trials. The sensor was then exchanged for either an unfatigued or fatigued plantarflexion sensor, depending upon which sensor the trials began with, and each trial was repeated by the participant using the opposite sensor. Results of the tests show that for both the static and dynamic movements, the capacitive output of the fatigued sensor was consistently higher than that of the unfatigued sensor suggesting that an upwards drift of the capacitance was occurring in the fatigued sensors. More research is needed to determine whether stretch sensors should be pre-stretched prior to data collection, and to also determine whether the drift stabilizes once the cyclic softening of the materials comprising the sensor has stabilized.