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Additive manufacturing (AM) offers new possibilities in soft robotics as materials can easily be combined in multi-material designs. Proper sensing is essential for the soft actuators to interact with the surroundings successfully. By fabricating sensors through AM, sensors can be embedded directly into the components during manufacturing. This paper investigates NinjaTek Eels electrical resistance response to strain and the feasibility of using the material to create strain sensors. Strain sensors were 3D-printed out of NinjaTek Eel, a soft conductive TPU, and was tested during cyclic loading. A custom resistance–strain test rig was developed for measuring sensor behavior. The rig was calibrated for electric resistance, able to measure electric resistance as a function of strain. A parabolic response curve was observed during cyclic loading, which led to ambiguous readings. A 10-specimen validation test was conducted, evaluating the statistical variation for the first 100 loading cycles. The validation test showed that the sensor is capable of accurate and predictable readings during single load cases and cyclic loading, with the overall root mean square error being 66.9 Ω. Combining two sensors of different cross-sections gave promising results in terms of calibrating. By monitoring load cycles and strain rates, calibration can also be achieved by machine learning models by the microcontroller used to extract data. The presented work in this article explores the potential of using conductive TPUs as sensors embedded in products such as soft robotics, life monitoring of products with structural, and digital twins for live product to user feedback. [ABSTRACT FROM AUTHOR]

Highlights The common performance optimization strategies of smart composite hydrogel are summarized. The recent advanced progress of smart composite hydrogel-based wearable sensors is systematically discussed from the aspect of health monitoring. The current challenges and future prospects of smart composite hydrogel-based wearable sensors are presented.

The fabrication of low-electrical-percolation-threshold polymer composites aims to reduce the weight fraction of the conductive nanomaterial necessary to achieve a given level of electrical resistivity of the composite. The present work aimed at preparing composites based on multiwalled carbon nanotubes (MWCNTs) and magnetite particles in a polyurethane (PU) matrix to study the effect on the electrical resistance of electrodes produced under magnetic fields. Composites with 1 wt.% of MWCNT, 1 wt.% of magnetite and combinations of both were prepared and analysed. The hybrid composites combined MWCNTs and magnetite at the weight ratios of 1:1; 1:1/6; 1:1/12; and 1:1/24. The results showed that MWCNTs were responsible for the electrical conductivity of the composites since the composites with 1 wt.% magnetite were non-conductive. Combining magnetite particles with MWCNTs reduces the electrical resistance of the composite. SQUID analysis showed that MWCNTs simultaneously exhibit ferromagnetism and diamagnetism, ferromagnetism being dominant at lower magnetic fields and diamagnetism being dominant at higher fields. Conversely, magnetite particles present a ferromagnetic response much stronger than MWCNTs. Finally, optical microscopy (OM) and X-ray micro computed tomography (micro CT) identified the interaction between particles and their location inside the composite. In conclusion, the combination of magnetite and MWCNTs in a polymer composite allows for the control of the location of these particles using an external magnetic field, decreasing the electrical resistance of the electrodes produced. By adding 1 wt.% of magnetite to 1 wt.% of MWCNT (1:1), the electric resistance of the composites decreased from 9 × 104 to 5 × 103 Ω. This approach significantly improved the reproducibility of the electrode's fabrication process, enabling the development of a triboelectric sensor using a polyurethane (PU) composite and silicone rubber (SR). Finally, the method's bearing was demonstrated by developing an automated robotic soft grip with tendon-driven actuation controlled by the triboelectric sensor. The results indicate that magnetic patterning is a versatile and low-cost approach to manufacturing sensors for soft robotics. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) offers new possibilities in soft robotics as materials can easily be combined in multi-material designs. Proper sensing is essential for the soft actuators to interact with the surroundings successfully. By fabricating sensors through AM, sensors can be embedded directly into the components during manufacturing. This paper investigates NinjaTek Eels electrical resistance response to strain and the feasibility of using the material to create strain sensors. Strain sensors were 3D-printed out of NinjaTek Eel, a soft conductive TPU, and was tested during cyclic loading. A custom resistance–strain test rig was developed for measuring sensor behavior. The rig was calibrated for electric resistance, able to measure electric resistance as a function of strain. A parabolic response curve was observed during cyclic loading, which led to ambiguous readings. A 10-specimen validation test was conducted, evaluating the statistical variation for the first 100 loading cycles. The validation test showed that the sensor is capable of accurate and predictable readings during single load cases and cyclic loading, with the overall root mean square error being 66.9 Ω. Combining two sensors of different cross-sections gave promising results in terms of calibrating. By monitoring load cycles and strain rates, calibration can also be achieved by machine learning models by the microcontroller used to extract data. The presented work in this article explores the potential of using conductive TPUs as sensors embedded in products such as soft robotics, life monitoring of products with structural, and digital twins for live product to user feedback.

Highlights: The common performance optimization strategies of smart composite hydrogel are summarized. The recent advanced progress of smart composite hydrogel-based wearable sensors is systematically discussed from the aspect of health monitoring. The current challenges and future prospects of smart composite hydrogel-based wearable sensors are presented. Growing health awareness triggers the public's concern about health problems. People want a timely and comprehensive picture of their condition without frequent trips to the hospital for costly and cumbersome general check-ups. The wearable technique provides a continuous measurement method for health monitoring by tracking a person's physiological data and analyzing it locally or remotely. During the health monitoring process, different kinds of sensors convert physiological signals into electrical or optical signals that can be recorded and transmitted, consequently playing a crucial role in wearable techniques. Wearable application scenarios usually require sensors to possess excellent flexibility and stretchability. Thus, designing flexible and stretchable sensors with reliable performance is the key to wearable technology. Smart composite hydrogels, which have tunable electrical properties, mechanical properties, biocompatibility, and multi-stimulus sensitivity, are one of the best sensitive materials for wearable health monitoring. This review summarizes the common synthetic and performance optimization strategies of smart composite hydrogels and focuses on the current application of smart composite hydrogels in the field of wearable health monitoring. [ABSTRACT FROM AUTHOR]

The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil. The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil. According to the requirement of mechanical flexibility of the sensor, the combined use of a layered flexible structural formation and a strain isolation layer is implemented. An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors. The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model. The stress distribution in the structure is investigated when bending load is applied to the structures. The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation (polydimethylsiloxane) layer accurately. The results by the proposed model are in good agreement with the finite element method, in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer. The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil. [ABSTRACT FROM AUTHOR]

Flexible and stretchable piezoresistive strain sensors which can translate mechanical stimuli (strain changes) into electrical signals (resistance changes) provide a simple and feasible detection tool in the field of health/damage monitoring, human motion detection, personal healthcare, human-machine interfaces, and electronic skin. Herein a detailed overview is presented on both strategies for sensing performance improvement and progress to medium or large-scale fabrication. A broad range of matrices/substrates and incorporated nanomaterials is covered and attention is paid to the current state-of-the-art of feasible but low-cost manufacturing methods. The sensor design parameters include sensitivity (gauge factor), stretchability, linearity, hysteresis, biocompatibility, and self-healing potential. Starting from fundamental sensing mechanisms, i.e. the tunneling effect, the disconnection mechanism, and the crack propagation mechanism, examples are provided from lab to application scale. [ABSTRACT FROM AUTHOR]