Foam-based soft actuators are lightweight and highly compressible, which make them an attractive option for soft robotics. A negative pressure drive would complement the advantages of foam actuators and improve the durability of the soft robotic system. In this study, a foam actuator was designed with a negative pressure pneumatic drive comprising bellows air chambers, a polyurethane foam body, and sealing layers at the head and tail. Experiments were performed to test the bending and contraction performances of the actuator with the foaming multiplier and air chamber length as variables. At air pressures of 0–90 kPa, the bending angle and contraction of the actuator increased with the foaming multiplier and number of air chamber sections. The designed actuator achieved a bending angle of 56.2° and contraction distance of 34 mm (47.9% of the total length) at 90 kPa, and the bending and contraction output forces were 3.5 and 7.2 N, respectively. A control system was built, and four soft robots were constructed with different numbers of actuators. In experiments, the robots successfully completed operations such as lifting, gripping, walking, and gesturing. The designed actuator is potentially applicable to debris capture, field rescue, and teaching in classrooms.
Purpose The purpose of this study was to fabricate silicone products that had different hardnesses and moduli, thus partially addressing the limitations of homogeneous materials whose deformation depends on altered structure or dimensions, and to provide new dimensions for the design of silicone soft structures. Design/methodology/approach A soft material three-dimensional printing platform with a dual-channel printing capability was designed and built. Using the material extrusion method, material screening was first performed using single-channel printing, followed by dual-channel-regulated printing experiments on products having different hardness and modulus values. Findings The proportion of additives has an effect on the accuracy of the printed product. Material screening revealed that Sylgard 527 and SE 1700 could be printed without additives. The hardness and mechanical properties of products are related to the percentage in their composition of hard and soft materials. The hardness of the products could be adjusted from 26A to 42A and the Young’s modulus from 0.875 to 2.378 Mpa. Originality/value Existing silicone products molded by casting or printing are mostly composed of a single material, whose uniform hardness and modulus cannot meet the demand for differentiated deformation in the structure. The existing multihardness silicone material printing method has the problems of long material mixing time and slow hardness switching and complicated multi-extrusion head switching. In this study, a simple, low-cost and responsive material extrusion-based hardness programmable preparation method for silicone materials is proposed.
The most common and easy approach to fabricating flexible strain sensors is based on the deposition principle. To improve the design of the sensing layer pattern, the reproducibility of the process and the sensitivity of the sensor, a controllable low-temperature-plasma spraying method for conducting nanoparticles was proposed. A flexible strain sensor was developed with multiwalled carbon nanotubes as the sensing layer and silica gel films as the substrate. The effects of plasma treatment on the cyclic stability and sensitivity of the sensor were examined and compared. The changes in the sensitivity of the sensor with the pattern parameters were also studied. The sensitivity of the sensor treated with low-temperature plasma was greatly improved (from 3.9 to 11.5) compared to that of an untreated sensor. In addition, pattern parameters significantly affected the rate of change in the resistance. A portable smart boxing glove prototype was developed using the prepared sensor and was then tested. The results showed that the smart glove could transmit and monitor a striking force of 49–490 N in real time with a sampling time, resolution, response time, and recovery time of 100 ms, up to 1.05 kg, 8 ms, and 150 ms, respectively.
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