A Sensorized Soft Pneumatic Actuator Fabricated with Extrusion-Based Additive Manufacturing

Pre-programmable pneumatic actuator: leveraging mechanical anisotropy of nonwoven fabrics with an integrated tensile sensor

The emergence of flexible fabric-based pneumatic actuators (FPAs) with pre-programmable motion capabilities, enhanced security and versatile interaction features significantly advances the construction of sophisticated soft robotic systems, owing to their enhanced security and versatile interaction features. Despite these promising attributes, the commercial viability of FPA products faces a considerable amount of challenges, primarily stemming from the scarcity of highly deformable fabric structures and the availability of industrial fabrication approaches. Taking inspiration from the anisotropic nature of lobster antennae, we propose a scalable and economical strategy to fabricate functional FPAs using nonwoven fabric material with superior mechanical anisotropy. This innovative method involves the adoption of tunable inelastic constrained wires sewn onto extensible nonwoven fabrics with regular wrinkles. This nonwoven fabric-based pneumatic actuator (NFPA) demonstrates specific motion profiles with curvature of over 0.6 cm-1 and output forces of over 140 cN under adjustable pressure conditions. Guided by the constrained wire combinations, NFPA enables diverse programmable motions like spiraling, assistance, and grasping. Furthermore, NFPA incorporated with specific sensors exhibits significant potential in wearable devices with real-time environmental detection for rehabilitation applications. Our work contributes a distinctive insight into the design of programmable NFPAs and enlightens an arena toward versatile soft robotic applications.

Influence of Manufacturing Process on the Conductivity of Material Extrusion Components: A Comparison between Filament- and Granule-Based Processes

The additive manufacturing of components using material extrusion (MEX) enables the integration of several materials into one component, including functional structures such as electrically conductive structures. This study investigated the influence of the selected additive MEX process on the resistivity of MEX structures. Specimens were produced from filaments and granules of an electrically conductive PLA and filled with carbon nanotubes and carbon black. Specimens were produced with a full-factorial variation of the input variables: extrusion temperature, deposition speed, and production process. The resistivity of the specimens was determined by four-wire measurement. Analysis of the obtained data showed that only the extrusion temperature had a significant influence on the resistivity of the MEX specimens. Furthermore, the impact of the nozzle diameter was evaluated by comparing the results of this study with those of a previous study, with an otherwise equal experimental setup. The nozzle diameter had a significant influence on resistivity and a larger nozzle diameter reduced the mean variance by an order of magnitude. The resistivity was lower for most process parameter sets. As the manufacturing process had no significant influence on the resistivity of MEX structures, it can be selected based on other criteria, e.g., the cost of feedstock.

3D Printable Modular Soft Elastomers from Physically Cross-linked Homogeneous Associative Polymers

Three-dimensional (3D) printing of elastomers enables the fabrication of many technologically important structures and devices. However, there remains a critical need for the development of reprocessable, solvent-free, soft elastomers that can be printed without the need for post-treatment. Herein, we report modular soft elastomers suitable for direct ink writing (DIW) printing by physically cross-linking associative polymers with a high fraction of reversible bonds. We designed and synthesized linear-associative-linear (LAL) triblock copolymers; the middle block is an associative polymer carrying amide groups that form double hydrogen bonding, and the end blocks aggregate to hard glassy domains that effectively act as physical cross-links. The amide groups do not aggregate to nanoscale clusters and only slow down polymer dynamics without changing the shape of the linear viscoelastic spectra; this enables molecular control over energy dissipation by varying the fraction of the associative groups. Increasing the volume fraction of the end linear blocks increases the network stiffness by more than 100 times without significantly compromising the extensibility. We created elastomers with Young's moduli ranging from 8 kPa to 8 MPa while maintaining the tensile breaking strain around 150%. Using a high-temperature DIW printing platform, we transformed our elastomers to complex, highly deformable 3D structures without involving any solvent or post-print processing. Our elastomers represent the softest melt reprocessable materials for DIW printing. The developed LAL polymers synergize emerging homogeneous associative polymers with a high fraction of reversible bonds and classical block copolymer self-assembly to form a dual-cross-linked network, providing a versatile platform for the modular design and development of soft melt reprocessable elastomeric materials for practical applications.

Soft Self-Regulating Heating Elements for Thermoplastic Elastomer-Based Electronic Skin Applications

Resistive heating elements can be of particular interest for many applications, such as e-skin. In this study, soft heating elements were developed by combining thermoplastic polyurethane (TPU) with carbon black. In contrast to previous studies on thermoplastic polymer-based thermistors, the heating elements could endure elongations above 100%. Due to the high melting point of the TPU and the carbon filler, the thermistors could be heated up to 180°C without significant deformation. The heating elements were extruded on TPU substrates using material extrusion additive manufacturing in one-step process. Self-regulating behavior to control the maximum temperature was achieved with the application of two different voltages (20 and 25 V) and different current thresholds, between 100 and 800 mA. The heating performance was adjusted by changing the geometry of the sensing elements; an increase in cross section resulted in a lower current density and lower temperature. For the heating elements, variation of the additive manufacturing parameters such as offset, layer height, nozzle speed, and extrusion multiplier resulted in a different width/height aspect ratio of the cross section of the extruded lines, affecting the initial resistivity of the thermistor. Orientation of the carbon filler during extrusion process is one reason for the small change of the longitudinal conductivity of the heating elements. The resulting skin with the integrated heating elements allowed the possibility to perform the in situ heating for the localized healing of structural damage, while maintaining the softness required for the application of soft robotic electronic skin.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning-based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross-talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8 mm, depth errors of 0.22 mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter-element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits

Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.

3D printing of soft fluidic actuators with graded porosity

New additive manufacturing methods are needed to realize more complex soft robots. One example is soft fluidic robotics, which exploits fluidic power and stiffness gradients. Porous structures are an interesting type for this approach, as they are flexible and allow for fluid transport. In this work, the infill foam (InFoam) method is proposed to print structures with graded porosity by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could exploit the repeatable coiling patterns to print structures. To this end, only the characterization of the relationship between nozzle height and coil radius and the extruded length was necessary (at a fixed temperature). Then by adjusting the nozzle height and/or extrusion speed the porosity of the printed structure could be set. The InFoam method was demonstrated by printing porous structures using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from 46% to 89%. In compression tests, the cubes showed large changes in compression modulus (more than 200 times), density (-89% compared to bulk), and energy dissipation. The InFoam method combined coiling and normal plotting to realize a large range of porosity gradients. This grading was exploited to realize rectangular structures with varying deformation patterns, which included twisting, contraction, and bending. Furthermore, the InFoam method was shown to be capable of programming the behavior of bending actuators by varying the porosity. Both the output force and stroke showed correlations similar to those of the cubes. Thus, the InFoam method can fabricate and program the mechanical behavior of a soft fluidic (porous) actuator by grading porosity.

3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing

The human tactile system is composed of multi-functional mechanoreceptors distributed in an optimized manner. Having the ability to design and optimize multi-modal soft sensory systems can further enhance the capabilities of current soft robotic systems. This work presents a complete framework for the fabrication of soft sensory fiber networks for contact localization, using pellet-based 3D printing of piezoresistive elastomers to manufacture flexible sensory networks with precise and repeatable performances. Given a desirable soft sensor property, our methodology can design and fabricate optimized sensor morphologies without human intervention. Extensive simulation and experimental studies are performed on two printed networks, comparing a baseline network to one optimized via an existing information theory based approach. Machine learning is used for contact localization based on the sensor responses. The sensor responses match simulations with tunable performances and good localization accuracy, even in the presence of damage and nonlinear material properties. The potential of the networks to function as capacitive sensors is also demonstrated.

Processing of Self-Healing Polymers for Soft Robotics

Soft robots are, due to their softness, inherently safe and adapt well to unstructured environments. However, they are prone to various damage types. Self-healing polymers address this vulnerability. Self-healing soft robots can recover completely from macroscopic damage, extending their lifetime. For developing healable soft robots, various formative and additive manufacturing methods have been exploited to shape self-healing polymers into complex structures. Additionally, several novel manufacturing techniques, noted as (re)assembly binding techniques that are specific to self-healing polymers, have been created. Herein, the wide variety of processing techniques of self-healing polymers for robotics available in the literature is reviewed, and limitations and opportunities discussed thoroughly. Based on defined requirements for soft robots, these techniques are critically compared and validated. A strong focus is drawn to the reversible covalent and (physico)chemical cross-links present in the self-healing polymers that do not only endow healability to the resulting soft robotic components, but are also beneficial in many manufacturing techniques. They solve current obstacles in soft robots, including the formation of robust multi-material parts, recyclability, and stress relaxation. This review bridges two promising research fields, and guides the reader toward selecting a suitable processing method based on a self-healing polymer and the intended soft robotics application.

3D Printing of Thermoplastic Elastomers: Role of the Chemical Composition and Printing Parameters in the Production of Parts with Controlled Energy Absorption and Damping Capacity

Additive manufacturing (AM) is a disruptive technology that enables one to manufacture complex structures reducing both time and manufacturing cost. Among the materials commonly used for AM, thermoplastic elastomers (TPE) are of high interest due to their energy absorption capacity, energy efficiency, cushion factor or damping capacity. Previous investigations have exclusively focused on the optimization of the printing parameters of commercial TPE filaments and the structures to analyse the mechanical properties of the 3D printed parts. In the present paper, the chemical, thermal and mechanical properties for a wide range of commercial thermoplastic polyurethanes (TPU) filaments were investigated. For this purpose, TGA, DSC, ¹H-NMR and filament tensile strength experiments were carried out in order to determine the materials characteristics. In addition, compression tests have been carried out to tailor the mechanical properties depending on the 3D printing parameters such as: infill density (10, 20, 50, 80 and 100%) and infill pattern (gyroid, honeycomb and grid). The compression tests were also employed to calculate the specific energy absorption (SEA) and specific damping capacity (SDC) of the materials in order to establish the role of the chemical composition and the geometrical characteristics (infill density and type of infill pattern) on the final properties of the printed part. As a result, optimal SEA and SDC performances were obtained for a honeycomb pattern at a 50% of infill density.

Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots

Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules.