This article is an extension of the IEEE sensor Conference 2022 paper and presents a study on the fabrication and integration of pressure sensors into rubber sealings. The sensors were used to continuously measure the...
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This article is an extension of the IEEE sensor Conference 2022 paper and presents a study on the fabrication and integration of pressure sensors into rubber sealings. The sensors were used to continuously measure the applied pressure and monitor the sealing's condition. The sensors are made using a single-layer polyimide-based piezoresistive material with gold interdigital electrodes. The working principle is the material conductivity change due to the rearrangement of conductive carbon nanotubes (CNTs) within the polymer caused by the applied pressure. The sensors were integrated into rubber sealings during their vulcanization process and it was found that higher vulcanization pressures suggest better integration as the air gap between the sensors and sealing is reduced. The sensor's performance is characterized by measurements of its resistance change due to the pressure response. The study shows that sensors with a 50- mu m interdigital electrode distance have a linear sensitivity up to a pressure of 55 N, while sensors with a larger (200 mu m) interdigital electrode distance have a lower linear region up to almost 10 N, but higher sensitivity. The thickness of the piezoresistive layer also affects the sensors' sensitivity, with thinner layers showing increased sensitivity but with a limited effective pressure region. The study shows that the relaxation phenomena of the rubber sealing can be measured with the sensor. Overall, the integration of sensors into sealings provides a real-time condition monitoring system that can detect faults and reduce the risk of environmental damage caused by fluid leakage, damage, and corrosion.
The calibration of capacitive soil moisture sensors is an essential step towards their integration into smart solutions. This study investigates the calibration of a widely used low-cost capacitive soil moisture senso...
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The calibration of capacitive soil moisture sensors is an essential step towards their integration into smart solutions. This study investigates the calibration of a widely used low-cost capacitive soil moisture sensor (SKU:SEN0193, DFRobot, Shanghai, China) in a loamy silt soil typically found in the Puglia region of Italy. The calibration function was derived from a random sample of 12 sensors, with three soil sample replicas per sensor, each of which had one of five gravimetric soil moisture contents, from relatively dry (5%) to full saturation (40%). The study reports the resulting calibration function along with the accuracy achieved with the generalized calibration function. The sensors proved to be accurate, with an R2 value ranging between 0.85 and 0.87 and a root mean square value (RMSE) ranging between 4.5 and 4.9%. The variation between the sensors was also investigated. The results showed that with higher soil moisture contents (above 30%), the sensor-to-sensor variability becomes significant, with a coefficient of variation (CV) ranging between 10 and 16%;meanwhile, in lower soil moisture contents, the CV ranged between 6.5 and 10.3%, implying that it is more consistent in lower moisture content within this soil condition. The resulting calibration function enhances the integration of such low-cost sensors into smart farming solutions. With proper calibration, these affordable capacitive sensors can achieve a high degree of accuracy, making them a viable option for widespread use in cost-effective precision agricultural applications.
A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of Sn...
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A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of SnO2- and WO3-based sensors. The six sensors, including SnO2- and WO3-based sensors and neural network algorithms, were used to measure gas mixtures. The six constituent sensors were subjected to acetone and hydrogen environments to monitor the effect of diet and/or irritable bowel syndrome (IBS) under the interference of ethanol. The SnO2- and WO3-based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO2- or WO3-based) are separately applied, even when deep learning is applied to enhance the sensing operation. However, hybrid integration is proven to be effective in discerning acetone from hydrogen even in a two-sensor configuration through the synergistic contribution of supervised learning, i.e., neural network approaches involving deep neural networks (DNNs) and convolutional neural networks (CNNs). DNN-based numeric data and CNN-based image data can be exploited for discriminating acetone and hydrogen, with the aim of predicting the status of an exercise-driven diet and IBS. The ramifications of the proposed hybrid sensor combinations and machine learning for the high-performance breath sensor domain are discussed.
The combination of flexibility and high integrability as well as their precise sensing capability enable dielectric elastomer sensors to be integrated into a variety of applications. Because of these integration oppor...
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The combination of flexibility and high integrability as well as their precise sensing capability enable dielectric elastomer sensors to be integrated into a variety of applications. Because of these integration opportunities especially in the field of smart textiles these sensor systems can make a difference. sensor inlays for shoes based on resistive and capacitive sensors enable a partial monitoring of the loads and load distributions on our feet while the interaction with the shoe shaft remains unknown. The novel sensorintegration into the shaft combined with a wireless real-time measurement is being studied in this work. This new sensor system within the shoe creates the basis for the data acquisition to finally support diabetic patients who suffer under the diabetic foot syndrome, to support athletes analyzing walking patterns as well as bringing safety shoes to a digital age. This contribution displays sensor concepts as well as the corresponding electronics development. The first smart shoe concentrates on the integration of six shaft sensors distributed over the shoe and their measurement in real-time. The shoe is designed and manufactured in collaboration with the Pruf- und Forschungsinstitut Pirmasens. The evaluation of the sensor signals allows the analysis of different load scenarios.
Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method ...
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Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method for integrating real sensor signals into RoboDK simulations, bridging the gap between virtual models and real-world dynamics. The proposed system utilizes an Arduino-based data acquisition module and a custom Python script to establish real-time communication between physical sensors and RoboDK's simulation environment. Unlike traditional simulations that rely on predefined simulated signals or manually triggered virtual inputs, our approach enables dynamic real-time interactions based on live sensor data. The system supports both analog and digital signals and is validated through latency measurements, demonstrating an average end-to-end delay of 23.97 ms. These results confirm the feasibility of real sensorintegration into RoboDK, making the system adaptable to various industrial applications. This framework provides a scalable foundation for researchers and engineers to develop enhanced simulation environments that more accurately reflect real industrial conditions.
A multivariable motion sensor is presented that embeds into its onboard microcontroller a tailored algorithm, referred to here as the double-path (DP) algorithm, which estimates velocity in real time from position and...
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A multivariable motion sensor is presented that embeds into its onboard microcontroller a tailored algorithm, referred to here as the double-path (DP) algorithm, which estimates velocity in real time from position and acceleration signals simultaneously measured by the sensor itself. The multivariable motion sensor consists of a contactless magnetic linear position digital sensor and a triaxial digital accelerometer. The proposed algorithm estimates velocity by suitably mixing the integration of the acceleration and the linear fitting of the position, and it can operate under both trapezoidal and S-curve motion profiles. The velocity estimation accuracy has been assessed through simulations and experimental tests, which involved performance evaluation and a comparative analysis between the proposed algorithm and a Kalman filter (KF) both embedded into the sensor microcontroller. The experimental results are obtained by operating the sensor with a reference trapezoidal motion profile with a maximum velocity of 50 mm/s. The two root-mean-square estimation errors calculated for the sensor moving at constant acceleration and velocity are 1.32% and 0.58% of the maximum velocity, respectively.
The recent advancement of artificial intelligence (AI), especially the Large Language Model and multimodal robot learning, promoted robotics development. The force/torque sensor is one of the essential components of r...
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The recent advancement of artificial intelligence (AI), especially the Large Language Model and multimodal robot learning, promoted robotics development. The force/torque sensor is one of the essential components of robotics application since it is used to give the force feedback under operation. With accurate force measurement, robots can perform delicate manipulation control and safe interaction with humans. This review article provides a comprehensive examination of force/torque sensor technologies. It highlights an in-depth analysis of sensing principles and calibration principles. In addition, the integration of the force/torque sensor and its fusion with other sensors for disturbance observer and collision detection is discussed. Some practical applications of force/torque sensors in robotics are thoroughly reviewed, illustrating their pivotal role in enhancing the functionality and interaction of robots in industrial automation, medical assistance, humanoid robotics, teleoperation, and embodied AI. Finally, the article explores future sensor development directions.
The heterogeneous integration of analog/digital mixed-signal circuits has become a key development trend in advanced microelectronic packaging. The high integration density within analog/digital mixed-signal microsyst...
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The heterogeneous integration of analog/digital mixed-signal circuits has become a key development trend in advanced microelectronic packaging. The high integration density within analog/digital mixed-signal microsystem has resulted in increasingly complex coupling between components, underscoring the critical importance of system health diagnostics. However, there is no good method available for in-situ monitoring of 2.5D/3D integrated micro-module yet. In this work, on-chip current monitoring is achieved for analog/digital micromodule with Anisotropic Magnetoresistance (AMR) current sensors embedded. A multi-field Finite Element Model (FEM) is established for sensor optimization, and the analog/digital micro-module with embedded AMR current sensors is successfully fabricated via custom-designed heterogeneous integration processes. The sensitivity of the AMR current sensor exceeds 1.1 mV/V/A, and its accuracy reaches 4 % when measuring DC currents ranging from 40 mA to 210 mA. By employing this method, the high accuracy mode recognition has been validated for the analog/digital micro-module.
A flexible temperature sensing system that can simultaneously detect the temperature and position has great significance for the development of industry, medicine, military, and aerospace. Traditional flexible thermal...
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A flexible temperature sensing system that can simultaneously detect the temperature and position has great significance for the development of industry, medicine, military, and aerospace. Traditional flexible thermal sensor array with high integration density or filling factor used to achieve this goal faces challenges such as the complex design and fabrication process, performance consistency, and multifunctional integration. In this work, we design and develop a flexible heat spot tracker (FHST) for the simultaneous measurement of temperature and position of a heat spot. Important considerations for the design of FHST include the realization of good and stable in-plane thermal conduction, and the ability of reducing the filling factor of the sensor array. Systematical experimental and theoretical studies demonstrate several unique advantages of FHST, most significantly the capability of simultaneously sensing the temperature and position of a heat spot through a sparse sensor array. The demonstrated platform adds to the portfolio of smart skin for advanced aircraft, integrated circuit chip, industrial equipment, and many others.
sensor miniaturization offers significant advantages, including enhanced SoC integration efficiency, reduced cost, and lightweight design. While the roll-to-roll printed electronics fabrication process is advantageous...
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sensor miniaturization offers significant advantages, including enhanced SoC integration efficiency, reduced cost, and lightweight design. While the roll-to-roll printed electronics fabrication process is advantageous for the mass production of sensors compared to the traditional MEMS technology, producing sensors that require air gap-based 3D structures remains challenging. This study proposes an integration of roll-to-roll gravure printing with a transferring and bonding method for touch sensor fabrication. Unlike previously reported methods for sacrificial layer removal, this approach prevents stiction issues, thus enabling sensor miniaturization and providing the flexibility to select materials that minimize sensitivity degradation during scaling. For the lower part of the sensor, Ag and BaSO4 were roll-to-roll gravure-printed on a flexible PET substrate to form the bottom electrode and dielectric layer, followed by BaSO4 spin coating on the sensor's anchor area to form a spacer. For the upper part, a water-soluble PVP sacrificial layer was roll-to-roll gravure-printed on another flexible PET substrate, followed by spin coating Ag and SU-8 to form the top electrode and the structural layer, respectively. The sacrificial layer of the upper part was removed with water to delaminate the top electrode and structural layer from the substrate, then transferred and bonded onto the spacer of the lower part. Touch sensors of three different sizes were fabricated, and their performances were comparatively analyzed along with that of an epoxy resin-based sensor, demonstrating that our sensor attained miniaturization while achieving relatively high sensitivity.
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