Search

In soft robotics, the fabrication of extremely soft structures capable of performing bio-inspired complex motion is a challenging task. In this paper, an innovative 3D printing of soft silicone structures with embedded shape memory alloy (SMA) actuators are proposed, which is completed in a single printing cycle from CAD files. The proposed custom-made 3D printing setup, based on the material extrusion (MEX) method, was used in conjunction with a cartesian pick and place robot (CPPR) to completely automate the fabrication of thick silicone skins (7 mm) with embedded shape memory alloy actuators. These structures were fabricated monolithically without any assembly tasks and direct human intervention. Taking advantage of the capability to 3D print different geometries, three different patterns were fabricated over the silicone skin, resulting in remarkable dynamic motions: an out-of-plane deformation (jumping of the structure from the x-y plane to the x-z plane) was achieved for the first-time employing silicone skin, to the best of the author’s knowledge. In addition, two process parameters (printing speed and build plate temperature) and the extruded silicone curing mechanisms were investigated to enhance the printing quality. This paper aims to advance the role of additive manufacturing in the field of soft robotics by demonstrating all the benefits that a low-cost, custom-made silicone 3D printer can bring to the table in terms of manufacturing soft bio-inspired structures.

This work presents the main steps of design and testing of a novel haptic interface and adaptive admittance control scheme for optimal regulation of the human–machine interaction in hand rehabilitation mediated by a smart system in virtual–reality environment. The prototype development is the result of an integrated HW/SW design and, moreover, the advantages from additive manufacturing techniques and mechanical properties of soft materials are exploited for the realization steps. Indeed, to make the interface smart, a network of piezo-resistive force sensors is embedded into the user’s command interface and the acquired signals are used for the adaptive regulation of human–machine interaction. Another distinctive feature of the haptic interface, which enables to identify this latter as a smart system, is the interaction control based on the estimation of the user’s intention within a novel scheme of adaptive admittance control. The enhanced training process in rehabilitation assisted by the haptic interface and virtual environment has been experimentally validated during a series of goal-directed tasks. The improvement of the motor performance of the user under the assistance of the adaptive admittance control has been experimentally evaluated. Further results show that the rehabilitation system supports the quantitative assessment of the robustness of the motor learning performance of the hand under the generation of haptic disturbances.

A new manufacturing wave concerning the Additive Manufacturing of sensors is spreading: several benefits, such as cost, time, and manual task reduction, can be achieved. The aim of the present research is the one-shot Additive Manufacturing of a low-cost capacitive sensor for liquid level sensing. The Material extrusion (MeX) technology was used to fabricate the proposed sensors (composed of a flexible substrate, two conductive electrodes, and a top flexible coverage), and a Design for Additive Manufacturing (DfAM) approach in conjunction with the 3D printing force analysis was performed. Very thin conductive tracks (0.5 mm) were manufactured to obtain a sensor having a final capacitance value of 125 pF, readable by common laboratory instrumentation. The sensor has been tested for the liquid level sensing using two different liquids, i.e., sunflower oil and distilled water, exhibiting very good sensitivity of 0.078 $$\frac{pF}{mm}$$ pF mm and 0.79 $$\frac{pF}{mm}$$ pF mm , respectively, with high repeatability, thus obtaining sensing performances comparable with that of more expensive sensors found in literature. Moreover, the proposed sensor showed high linearity (R2 ≥ 0.997), which resulted in a maximum propagated level error of 1.4 mm. The present research proves that the inexpensive MeX technology can be successfully employed for the fabrication of high-performance capacitive sensors: the sensor manufacturing cost (related to raw materials) is 0.38 €, and no manual assembly tasks were performed. This study lays the foundation for the one-shot fabrication of smart structures with capacitive sensors on board, saving manufacturing time and cost.

A main challenge in the additive manufacturing (AM) field is the possibility to create structures with embedded actuators and sensors: addressing this requirement would lead to a reduction of manual assembly tasks and product cost, pushing AM technologies into a new dimension for the fabrication of assembly-free smart objects. The main novelty of the present paper is the one shot fabrication of a 3D printed soft finger with an embedded shape memory alloy (SMA) actuator and two different 3D printed sensors (strain gauge and capacitive force sensor). 3D printed structures, fabricated with the proposed approach, can be immediately activated after their removal from the build plate, providing real-time feedback because of the embedded sensing units. Three different materials from two nozzles were extruded to fabricate the passive elements and sensing units of the proposed bioinspired robotic finger and a custom-made Cartesian pick and place robot (CPPR) was employed to integrate the SMA spring actuator into the 3D printed robotic finger during the fabrication processes. Another novelty of the present paper is the direct integration of SMA actuators during the 3D printing process. The low melting thermoplastic polycaprolactone (PCL) was extruded: its printing temperature of 70 °C is lower than the SMA austenitic start temperature, preventing the SMA activation during the manufacturing process. Two different sensors based on the piezoresistive principle and capacitive principle were studied, 3D printed and characterized, showing respectively a sensitivity ratio of change in resistance to finger bending angle to be 674.8 $$\frac{\Omega }{^\circ \mathrm{Angle}}$$ Ω ∘ Angle and a capacitance to force ratio of $$0.53 \frac{\mathrm{pF}}{\mathrm{N}}$$ 0.53 pF N . The proposed manufacturing approach paves the way for significant advancement of AM technologies in the field of smart structures with embedded actuators to provide real-time feedback, offering several advantages, especially in the soft robotics domain.

In recent years, the exploitation of Additive Manufacturing technologies for the fabrication of different kinds of sensors has abruptly increased: in particular, a growing interest for extrusion-based techniques has emerged. This research proposes the exploitation of Fused Filament Fabrication (FFF) process and two commercial materials (one flexible and one conductive) for the monolithic fabrication of a bendable, coplanar capacitive sensor. The whole sensor, consisting of a flexible substrate and two electrodes, has been fabricated in a single-step printing cycle: Design for Additive Manufacturing approach was used, setting out a methodology to direct 3D print thin and close tracks with conductive materials, in order to obtain high capacitance values measurable by common measurement instrumentations. Despite a huge exploitation of FFF technology for piezoresistive-based sensors, this manufacturing process has never been used for the fabrication of coplanar capacitive sensors since the manufacture of thin and close conductive tracks (key requirement in coplanar capacitive sensors) is a challenging task, mainly due to low manufacturability of extruded conductive beads with a high level of detail. Two versions of the sensor were developed: the first one with an embedded 3D printed coverage (ready to use) and the second one which requires a further manual post-processing to seal the electrodes. The main benefits related to the exploitation of FFF technology for these sensors are: i) the reduction of the number of different manufacturing processes employed, from at least two in traditional manufacturing approach up to one, ii) the exploitation of a cost-effective technology compared to traditional high-cost technologies employed (i.e. lithography, inkjet etc.) iii) the reduction of manual and assembly tasks (one of the proposed versions does not require any further task) , and iv) the cost-effectiveness of the sensors (in a range between 0.27 € and 0.38 €). The two developed prototypes have been tested demonstrating all their potentialities in the field of liquid level sensing, showing results consistent with the ones found in scientific literature: good sensitivity and high linearity and repeatability were proved when different liquids were employed. These 3D printed liquid level sensors have these features: i) flexible sensor, ii) the length is limited only by the machine workspace, iii) they can be either applied outside of the traditional reservoirs or embedded into the reservoirs (by 3D printing both the reservoir and sensor in the same manufacturing cycle), and iv) simple calibration.Finally, the bendability of these sensors paves the way toward their application for liquid level sensing into tanks with non-conventional shapes and for other application fields (i.e. soft robotics, non-invasive monitoring for biomedical applications).

The exploitation of Additive Manufacturing (AM) or 3D printing in the field of the sensor fabrication is abruptly rose in recent years. In this paper, new sensors manufactured using Fused Filament Fabrication (FFF) technology and two different commercial conductive filaments have been characterized from a thermal point of view. Using a climatic chamber to vary the temperature in a range between −5 °C and 50 °C, several tests were carried out showing a strong link between resistance and temperature changes. In particular, a positive correlation between resistance and temperature occurs. Different aspects due to the nature of the conductive 3D printed materials, nonconductive support structure, and the role of the air have been outlined, proving the need for subsequent thermal tests in order to exploit these low-cost devices as temperature sensors in the future.

In this paper a new low-cost stretchable coplanar capacitive sensor for liquid level sensing is presented. It has been 3D-printed by employing commercial thermoplastic polyurethane (TPU) and conductive materials and using a fused filament fabrication (FFF) process for monolithic fabrication. The sensor presents high linearity and good repeatability when measuring sunflower oil level. Experiments were performed to analyse the behaviour of the developed sensor when applying bending stimuli, in order to verify its flexibility, and a thermal characterization was performed in the temperature range from 10 °C to 40 °C to evaluate its effect on sunflower oil level measurement. The experimental results showed negligible sensitivity of the sensor to bending stimuli, whereas the thermal characterization produced a model describing the relationship between capacitance, temperature, and oil level, allowing temperature compensation in oil level measurement. The different temperature cycles allowed to quantify the main sources of uncertainty, and their effect on level measurement was evaluated.

Fused Filament Fabrication is the most widespread 3D printing process, and issues such as improving accuracy and speed are significant areas of research. To better understand and foresee the process, accurate models would be very useful. Several analytical models have been proposed in the literature; however, while the behavior inside the print core has been investigated, to the best of the authors’ knowledge, no studies are reported on the behavior of the melt filament downstream of the nozzle. This lack of research is very important, since the behavior downstream of the nozzle is influenced by the counterpressure generated by the deposited material. Qualitatively, the lower the layer height, the higher the counterpressure should be, while the higher the printing speed, the lower the counterpressure. No such models are available in the literature that take these phenomena into account, and such a model would help in managing the printing process when low layer height is required for improving the accuracy and roughness of the part. In the present study, a new analytical model was developed to compute the minimum force necessary to push the filament into the extruder according to given values of printing process parameters. The model considers both the contribution of the extrusion force and of the deposition force, allowing the prediction of the variation of the required pushing force when variations of the layer height occur and can be a useful tool in the design of process parameters when very accurate components are needed in the process of additive manufacturing (AM).

Nowadays, a challenging scenario involving additive manufacturing (AM), or 3D printing, relates to concerns on the manufacturing of electronic devices. In particular, the possibility of using fused filament fabrication (FFF) technology, which is well known for being very widespread and inexpensive, to fabricate structures with embedded sensing elements, is really appealing. Several researchers in this field have highlighted the high electrical resistance values and variability in 3D-printed strain sensors made via FFF. It is important to find a way to minimize the electrical resistance and variability among strain sensors printed under the same conditions for several reasons, such as reducing the measurement noise and better balancing four 3D-printed strain gauges connected to form a Wheatstone bridge to obtain better measurements. In this study, a design of experiment (DoE) on 3D-printed strain gauges, studying the relevance of printing and design parameters, was performed. Three different commercial conductive materials were analyzed, including a total of 105 printed samples. The output of this study is a combination of parameters which allow both the electrical resistance and variability to be minimized; in particular, it was discovered that the “welding effect” due to the layer height and printing orientation is responsible for high values of resistance and variability. After the optimization of printing and design parameters, further experiments were performed to characterize the sensitivity of each specimen to mechanical and thermal stresses, highlighting an interesting aspect. A sensible variation of the electrical resistance at room temperature was observed, even if no stress was applied to the specimen, suggesting the potential of exploiting these materials for the 3D printing of highly sensitive temperature sensors.

In this paper we present the exploitation of Fused Filament Fabrication (FFF) to manufacture a load cell using double extrusion of conductive and non-conductive commercial materials in a single-step printing cycle. A load cell with four embedded strain gauges, manufactured with tailored process parameters and strategies, was used to deposit the conductive filament to obtain near equal electrical resistance values among the four strain gauges, aiming to connect them in a full Wheatstone bridge configuration. Subsequently, several tests were performed, firstly to understand the behavior of each strain gauge and then to characterize the load cell. The tests showed that the strain gauges are sensible to compressive and tensile deformation and that the load cell's voltage, obtained by connecting the four strain gauges in a full Wheatstone bridge, decreases as the force applied increases. This work demonstrates the potential of FFF technology in the sensor manufacturing field and that it is possible to integrate sensitive elements into non-sensitive elements without an additional assembly process by using low-cost commercial filaments and 3D printers.

Soft robotics is an emerging scientific field well known for being widespread employed in several applications where dexterity and safe interaction are of major importance. In particular, a very challenging scenario in which it is involved concerns bio-medical field. In the last few years, several soft robotic devices have been developed to assist elderly people in daily tasks. In this paper, the authors present a new manufacturing approach for the fabrication of I-SUPPORT, a soft arm used to help needful people during shower activities. The proposed I-SUPPORT version, based on pneumatic and cable-driven actuation, is manufactured using Fused Filament Fabrication (FFF), the most common and inexpensive Additive Manufacturing (AM) technology. The advantages offered by FFF technology compared to traditional manufacturing methods regard: (i) the possibility to increase the automation degree of the process by reducing manual tasks, (ii) the decrease of assembly operations and (iii) an improvement in terms of supply chain. Moreover, the constitutive I-SUPPORT elements have been printed separately to save time, reduce materials and optimize the waste in case of failure. Afterwards, the proposed soft robotic arm has been tested to evaluate the performances and of the chambers, module and the whole I-SUPPORT manipulator.

In this paper a low-cost load cell with four embedded strain gauges has been developed using Fused Deposition Modeling (FDM) technology. Two commercial materials, conductive and non-conductive, have been used to manufacture the sensor in a single and non-stop printing cycle. Afterwards, each strain gauge has been characterized by evaluating the resistance change in relation to the applied force and as a consequence, it has been found that they are sensible to deformations due to compression and tension. Hence, it has been proved that with this manufacturing approach it is possible to integrate sensitive elements such as strain gauges into nonsensitive elements to create low-cost sensors using FDM, one of the most common 3D printing technology.

In the 21st century, robotics has been revolutionized by a new perspective: robots made of soft materials mimicking nature show several advantages compared to hard robot counterparts, leading to the birth and quick spread of a new scientific field named soft robotics. Due to the unconventional materials used in soft robotics (not only soft materials but also conductive, magnetic, and other materials employed for soft robot actuation), the manufacturing process is the basis by which to obtain soft structures able to meet specific requirements and save time and cost. The present paper aims to give a review of the current use of additive manufacturing (AM) methods for the manufacturing of soft robots. AM, also known as 3D printing, is characterized by several features, such as the possibility to easily produce very complex geometries, which are well suited for soft robot fabrication. The authors, after providing a summary of the most used actuation systems in soft robots, classify the use of AM technologies in soft robotics, investigating the most important and recent works in this field, as a function of how these technologies are employed in the whole soft robot manufacturing cycle. With this in mind, the authors found three approaches to exploit AM in soft robotics: rapid mold fabrication (AM plays a passive role), hybrid (AM plays an active role but manufacturing technologies other than AM are still used), and total additive manufacturing (only AM technologies are used). Only the latter approach takes full advantage of AM, although the other two still provide benefits. With new developments in commercial 3D printing systems (machines and materials) aimed at the fabrication of soft and unconventional parts, AM is set to become the manufacturing technology of the future in the soft robotics field, overcoming traditional manufacturing methods such as molding and soft lithography.