Polyurethane shape memory filament yarns: Melt spinning, carbon-based reinforcement, and characterization

The aim of this work was to develop and characterize polyurethane-based shape memory polymer filament yarns of a suitable diameter and thermo-mechanical performance for use in tailored multi-sectorial applications. Different polymer compositions – pure shape memory polyurethane and shape memory polyurethane composites with 0.3 and 0.5 wt.% of multi-walled carbon nanotubes or carbon black as additives – were studied. Filaments were obtained using a melt spinning process that allowed the production of the permanent and temporary shape of the shape memory polyurethane filament. Two drawing speeds (20 and 32¿m/min) were studied. Characterization techniques such as the tensile test, differential scanning calorimetry, and dynamic mechanical analysis were used to investigate the shape-memory effect of the filaments. Pure and additive shape memory polyurethane filament yarns of a controlled diameter were produced. The results indicated that the pure shape memory polyurethane on the temporary shape had the highest tensile strength (234¿MPa). Filaments with carbon black revealed a significant strain (335%) in the permanent shape with respect to the other filaments. The melt spinning process influenced the soft segment glass transition temperature (Tgs) significantly, with a decrease in the temporary shape (first heating) as compared to the permanent shape (second and third heating). However, only the 0.5% multi-walled carbon nanotubes additive clearly influenced the filament, increasing the Tgs by 10°C. The additives also influenced the shape-memory effect, obtaining an increased fixity ratio (up to 97%) with the multi-walled carbon nanotubes additive and an increased recovery ratio (up to 86%) with the carbon black additivePostprint (author's final draft

Adaptive reversible composite-based shape memory alloy soft actuators

This research demonstrates how a combination of two-way shape memory alloy (SMA), low-temperature liquid epoxy cure composites, and fibre reinforced plastic (FRP) may be utilised to create a novel reversible actuator with built driven functionality. The novelty of this work is that the actuator can reverse its original shape and be mounted on different customized structures. The strategy is based on a knowledge of SMA wires and the manufacturing principle underlying composite structure, as well as experiments to see how soft SMA-FRP can be programmed to bend. The folding mechanism is studied in terms of fabrication factors such as SMA training and strong interfacial bonding between SMA and epoxy resin, which influence the programming process and shape change. The two-way SMA wires are trained using the pre-straining method to programme the SMAs. The technique has been used to assemble the SMA wires with bond reliability to enhance the actuator interface's thermal behaviour. The SMA elements are directly inserted into FRP strips and epoxy resin is used as an adhesive, resulting in dynamic hybrid composites. The module is actuated using an electrical board with a current value between 3 and 6 A. The robustness, controllability, mechanical properties, and 500 life cycles of the actuator are tested. Results indicate a bending angle of 58° with 30 mm of deflection in 7 s after actuating the module. Also, 3D printing is used to print a gripper inspired by human fingers and a structure to lift various weights. The actuator’s performance as a soft gripper is reliable in terms of grasping objects of different shapes

Controllable and reversible tuning of material rigidity for robot applications

Tunable rigidity materials have potentially widespread implications in robotic technologies. They enable morphological shape change while maintaining structural strength, and can reversibly alternate between rigid, load bearing and compliant, flexible states capable of deformation within unstructured environments. In this review, we cover a range of materials with mechanical rigidity that can be reversibly tuned using one of several stimuli (e.g. heat, electrical current, electric field, magnetism, etc.). We explain the mechanisms by which these materials change rigidity and how they have been used for robot tasks. We quantitatively assess the performance in terms of the magnitude of rigidity, variation ratio, response time, and energy consumption, and explore the correlations between these desired characteristics as principles for material design and usage

Designing LMPA-Based Smart Materials for Soft Robotics Applications

This doctoral research, Designing LMPA (Low Melting Point Alloy) Based Smart Materials for Soft Robotics Applications, includes the following topics: (1) Introduction; (2) Robust Bicontinuous Metal-Elastomer Foam Composites with Highly Tunable Mechanical Stiffness; (3) Actively Morphing Drone Wing Design Enabled by Smart Materials for Green Unmanned Aerial Vehicles; (4) Dynamically Tunable Friction via Subsurface Stiffness Modulation; (5) LMPA Wool Sponge Based Smart Materials with Tunable Electrical Conductivity and Tunable Mechanical Stiffness for Soft Robotics; and (6) Contributions and Future Work.Soft robots are developed to interact safely with environments. Smart composites with tunable properties have found use in many soft robotics applications including robotic manipulators, locomotors, and haptics. The purpose of this work is to develop new smart materials with tunable properties (most importantly, mechanical stiffness) upon external stimuli, and integrate these novel smart materials in relevant soft robots. Stiffness tunable composites developed in previous studies have many drawbacks. For example, there is not enough stiffness change, or they are not robust enough. Here, we explore soft robotic mechanisms integrating stiffness tunable materials and innovate smart materials as needed to develop better versions of such soft robotic mechanisms. First, we develop a bicontinuous metal-elastomer foam composites with highly tunable mechanical stiffness. Second, we design and fabricate an actively morphing drone wing enabled by this smart composite, which is used as smart joints in the drone wing. Third, we explore composite pad-like structures with dynamically tunable friction achieved via subsurface stiffness modulation (SSM). We demonstrate that when these composite structures are properly integrated into soft crawling robots, the differences in friction of the two ends of these robots through SSM can be used to generate translational locomotion for untethered crawling robots. Also, we further develop a new class of smart composite based on LMPA wool sponge with tunable electrical conductivity and tunable stiffness for soft robotics applications. The implications of these studies on novel smart materials design are also discussed