Embedding Sensors Research Articles

Investigating the mechanical properties of composites containing exfoliated graphite/epoxy sensors: Experimental testing and simulation

One approach to structural health monitoring (SHM) involves embedding sensors within a composite material. However, the integration of these sensors can potentially introduce flaws that might affect the composite’s mechanical properties. This research aims to explore the impact of embedding exfoliated graphite (EG)/epoxy sensors on the mechanical characteristics of composite systems through laboratory experiments and numerical simulations. Sensor strips composed of varying volume fractions of EG/epoxy were fabricated. Tensile test specimens were prepared by embedding these sensors in the epoxy matrix oriented both lengthwise and widthwise. Baseline specimens of EG/epoxy without sensors were also created for comparison. Tensile tests were performed on the samples to evaluate the effects of the embedded sensors on the composite’s elastic modulus and tensile strength. The results indicated a slight improvement in both elastic modulus and tensile strength with the introduction of EG. Crucially, the orientation of the sensors within the samples had a significant impact on the composite’s mechanical properties. Samples with widthwise-aligned sensors showed reduced tensile strength due to delamination along the sensor edges. Finite element simulations using a viscoelastic model based on the experimental data were conducted to analyze the effect of sensor alignment on mechanical properties. The findings revealed that a grid pattern alignment of sensors significantly enhanced mechanical performance compared to lengthwise or widthwise alignment, particularly at 0.1% and 0.3% EG volume fractions, highlighting the effectiveness of a grid pattern for embedding sensors in SHM applications.

Spot pattern welding scanning strategy for sensor embedding and residual stress reduction in laser-foil-printing additive manufacturing

Spot pattern welding scanning strategy for sensor embedding and residual stress reduction in laser-foil-printing additive manufacturing

Sandwich-structured electrospun polyvinylidene difluoride sensor for structural health monitoring of glass fiber reinforced polymer composites

Stress monitoring and interlaminar failure detection attract much attention in glass fiber reinforced polymer (GFRP) structural health monitoring area. However, due to limitations of sensing or electrode materials, existing embedding sensors cannot be designed to have both of the abilities without sacrificing mechanical properties. This work fabricated a sandwich-structured sensor, which is composed of three layers of electrospun polyvinylidene difluoride membranes. The upper and lower electrodes are flexible membranes that ensure stable signal collection in the rigid GFRP material system. The sensor can quantitatively monitor the stress based on piezoelectric effect, and interlaminar crack propagation based on parallel plate capacitors. The voltage and capacitance values have a linear relationship with the stress level and crack length (R-square of the functions is 0.999 and 0.933), respectively. Due to the porous microstructure of electrospun membranes, polymer matric can well infiltrate the polyvinylidene difluoride nanofibers while preparing the sensor embedded GFRP. Thus, the perfect bonding of the sensor within GFRP ensures the effective sensing abilities until sample failure and negligible effect (< -7%) on the mechanical properties of GFRP.

Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing

Microstructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes.

Learning control for body caudal undulation with soft sensory feedback

Soft bio-mimetic robotics is a growing field of research that seeks to close the gap with animal robustness and adaptability where conventional robots fall short. The embedding of sensors with the capability to discriminate between different body deformation modes is a key technological challenge in soft robotics to enhance robot control–a difficult task for this type of systems with high degrees of freedom. The recently conceived Linear Repetitive Learning Estimation Scheme (LRLES)–to be included in the traditional Proportional–integral–derivative (PID) control–is proposed here as a way to compensate for uncertain dynamics on a soft swimming robot, which is actuated with soft pneumatic actuators and equipped with soft sensors providing proprioceptive information pertaining to lateral body caudal bending akin to a goniometer. The proposed controller is derived in detail and experimentally validated, with the experiment consisting of tracking a desired trajectory for the bending angle envelope while continuously oscillating with a constant frequency. The results are compared vis a vis those achieved with the traditional PID controller, finding that the PID endowed with the LRLES outperforms the PID controller (though the latter has been separately tuned) and experimentally validating the novel controller’s effectiveness, accuracy, and matching speed.

HFN: Heterogeneous feature network for multivariate time series anomaly detection

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies.

Experimental investigation on damage of concrete beam embedded with sensor using acoustic emission and digital image correlation

With the construction and development of intelligent roads, an increasing number of sensors are being embedded into the road structures. Portland cement concrete (PCC) pavement is one of the main forms of road pavement, but the mechanical behaviors of the PCC pavement with sensor embedding are less studied. In this study, the damage mechanism of PCC pavement is investigated by using the three-point beam bending test, considering the sensor embedding and the coating of the sensor surface. The effect of sensor embedding on the flexural mechanical properties and the damage failure modes of concrete beams are evaluated by using the acoustic emission (AE) technique and the digital image correlation (DIC) technique. Moreover, the concept of degree of coupling (DC) is introduced to reveal the collaborative bearing mechanism between sensor and concrete, and is characterized by an evaluation model considering ultimate bearing capacity, critical instability point, collaborative bearing capacity, AE ring counts, and AE energy. The findings can provide valuable insights in the packaging design of sensor to guarantee the coupling between the sensor and the surrounding concrete for accurately monitoring the long-term performance of road infrastructure.

An experimental study on dynamic response of cement concrete pavement under vehicle load using IoT MEMS acceleration sensor network

Understanding the dynamic response of pavements is crucial for comprehending pavement deformation and interaction mechanisms under vehicle load. There is a notable absence of monitoring systems that involve the in-situ embedding of acceleration sensors within full-scale cement concrete pavement slabs. This research introduces an IoT system designed for the monitoring of pavement vibration responses, with its deployment and installation executed within a field engineering project. By collecting and analyzing the pavement vibration responses to vehicle loads, the study investigates the time–frequency characteristics of vibration signals across various vehicle speeds, sensor depths, and lateral distances. The findings reveal that both the amplitude and main frequency of the vibration signal escalate with vehicle speed, whereas the embedding depth of the sensor impacts only the amplitude, leaving the main frequency unaffected. Through statistical analysis, it was determined that effective sensor spacing on cement concrete pavements should not exceed 120 cm to avoid significant signal attenuation, which on average reaches 50 % at a sensor spacing of 90 cm. These insights offer critical guidance for optimizing sensor layouts on cement concrete pavements and lay the foundation for future research on vibration-based damage detection and vehicle information analysis.

A comparative analysis of acoustic emission sensor embedding in glass fibre composite

The manufacturing process of composite structures permits fully embedding acoustic emission (AE) sensors. While the embedding process may pose challenges, its advantages, if proven, can outweigh the challenges. The increased sensitivity resulting from embedding acoustic emission sensors in composites is still not definitively established. A test was set up with pre-determined AE initiation locations (surface and sub-surface) and pre-determined receiving sensor's location (surface and sub-surface) to ensure any sensitivity increase was evident. The receiving sensor's attenuation along (at 90°) and across the fibres (at 45°) was assessed using two test methods: pencil lead breaking (PLB) and actuator methods. The actuator method involved using two pulse generators, the TGP110 pulse generator and the Mistras FieldCal. A range of specific frequencies were utilised, 30, 60, 150 and 300 kHz, using the FieldCal. The results obtained from the test methods were not in agreement with each other. For example, comparing the sensitivity using surface cracks, the PLB method showed decreased sensitivity when embedding the receiving sensor compared to the actuator method, which demonstrated minimal changes in sensitivity. The research aims to clarify the sensitivity increase obtained when embedding an AE sensor while taking into account the crack's position and frequency.

Embedding a surface acoustic wave sensor and venting into a metal additively manufactured injection mould tool for targeted temperature monitoring

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) “stop-and-go” approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

Unsaturated out-of-plane permeability measured with three-dimensional flow monitoring by etched optical fiber sensors

In our work, the three-dimensional flow in stacks of biaxial E-glass fiber non-crimp fabrics (NCF) and unidirectional carbon fiber layers was monitored by the etched optical fiber sensors embedded inside the fabric stack. The influence of embedding sensors with a diameter of 200 µm on the flow was experimentally illustrated and discussed. The out-of-plane permeability of the two fabric materials was estimated with a radial infusion model using the in-plane permeability values as the prior knowledge. The assumed radius of the semi-ellipsoidal flow was derived and showed a good agreement with the results obtained by the best fit of the exact solution, which provides an approximate solution that simplifies the calculation process. The asymptotic form and the modified semi-ellipsoidal inlet radius method for the case of a non-negligible radius of injection tube compared to the fabric sample thickness were verified. The uncertainties related to the unknown flow pattern near the inlet were also demonstrated. The presented results facilitate the further development and standardization of out-of-plane permeability measurement methods with embedded sensors.

Embedding of Fiber Optic Sensors in Metal Parts by Laser Welding and Additive Manufacturing: A Review

Embedding of Fiber Optic Sensors in Metal Parts by Laser Welding and Additive Manufacturing: A Review

Non-additive polymer matrix coated rGO/MXene inks for embedding sensors in prepreg enhancing smart FRP composites

Two-dimensional (2D) nanosheets are promising nanomaterials that can be used as sensing elements in numerous applications such as wearable devices, machine tools, and aircraft; however, challenges remain in achieving high sensitivity, wide frequency sensing range, and reliability after long cyclic loading. Suitable ink formulations and preparation parameters for embedded sensors while retaining the structural integrity of the host structure should be determined. In this work, we demonstrated a synergistic combination of l-cysteine-reduced graphene oxide and MXene (LGM) inks in the absence of an additional polymer matrix to establish noninvasive conditions for embedding sensors in prepreg fiberglass/epoxy for smart composites with sensing capabilities. This sensor offers a high gauge factor of 47–1400 in different tensile strain ranges, with unnoticeable changes in the outstanding mechanical properties of the host glass fiber-reinforced polymer (GFRP) laminate. Long-term fatigue three-point bending tests were performed at different frequencies and bending angles, demonstrating excellent durability and stability. The corresponding sensors can also capture vibration signals in the low-high-frequency ranges. The nonintrusive embedment of sensors in GFRP laminates using the new coated non-additive polymer matrix LGM ink successfully preserves the mechanical properties of the host, including its structural integrity, with outstanding sensing performance for low-to-high-frequency signals.

Influence of Temperature–Humidity Sensor Housing Depth on Concrete and Mortar Compressive Strength

Recent advancements in sensor technology have led to an increase in embedding sensors into construction materials for monitoring purposes in the construction industry. However, systematic research on the resulting changes to the material properties is still lacking. A previous study confirmed that the copper–nickel-plated housing of SHT-31 sensors affects the compressive strength of mortar. Moreover, it is necessary to conduct further research to determine if this analogous occurrence takes place in concrete. This study embedded temperature–humidity sensor housings in concrete at 10 mm, 20 mm, and 30 mm, before performing compression tests and a finite element analysis (FEA). The empirical findings indicate that the compressive strengths of concrete at 10 mm, 20 mm, and 30 mm depths were 26.1 MPa, 28.4 MPa, and 29.4 MPa, respectively. In contrast, the control concrete that did not have a sensor housing had a compressive strength of 31.9 MPa. In the case of mortar, a design strength of 28 MPa was achieved at a depth greater than 30 mm, while concrete reached this design strength at 20 mm. Based on these findings, embedding temperature–humidity sensor housings in concrete is recommended at depths greater than 20 mm from the surface. These results serve as important reference data for determining the optimal embedding depth of sensor housings in structures using cement-based materials.

Inertial measurement data from loose clothing worn on the lower body during everyday activities

Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a ‘sensor string’ and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28–48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5–8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features.

VibWall : Smartphone’s Vibration Challenge-response for Wall Crack Detection

As the building ages, the wall structure may become deteriorated (e.g., wall cracks, discontinuities, and corrosion) due to the variation of the environment (i.e., temperature and humidity). Moreover, these wall cracks, discontinuities, and corrosion will affect the living comfort and coziness. As such, the wall health diagnostic becomes crucial for the safety and comfort of modern buildings. However, the existing wall health detection techniques (e.g., UWB radars, acoustic sensing, and sensor embedding techniques) are high-cost, not ubiquitous, and not robust to the variation of the environment. In this article, we propose VibWall , a system that can use the smartphone’s sensors (i.e., accelerometer, gyroscope, and vibrator) to detect the wall’s structural health. Specifically, the wall cracks can be detected for living safety, comfort, and coziness. Our key idea is that the smartphone’s vibration is absorbed, reflected, and propagated disparately based on the physical structure of the wall. To be specific, we employ a novel challenge-response scheme, where the challenge is a sequence of heterogeneous vibration patterns from the smartphone’s vibrator, and the responses to these vibrations are sensed by the smartphone’s gyroscope and accelerometer sensors. Then, the machine learning-based classifier (e.g., random forest classifier) will be used to discriminate between the healthy wall and the wall with cracks, discontinuities, or corrosion based on these responses. Our experimental results show good performance on the wall’s structural health detection with the wall specimen and real-world walls.

Soft pneumatic actuators with integrated resistive sensors enabled by multi-material 3D printing

The concept of soft robots has garnered significant attention in recent studies due to their unique capability to interact effectively with the surrounding environment. However, as the number of innovative soft pneumatic actuators (SPAs) continues to rise, integrating traditional sensors becomes challenging due to the complex and unrestricted movements exhibited by SPA during their operation. This article explores the importance of utilising one-shot multi-material 3D printing to integrate soft force and bending sensors into SPAs. It highlights the necessity of a well-tuned and robust low-cost fabrication process to ensure the functionality of these sensors over an extended period. Fused deposition modelling (FDM) offers a cost-effective solution for embedding sensors in soft robots, directly addressing such necessity. Also, a finite element method (FEM) based on the nonlinear hyper-elastic constitutive model equipped with experimental input is developed to precisely predict the deformation and tip force of the actuators measured in experiments. The dynamic mechanical test is conducted to observe and analyse the behaviour and resistance changes of conductive thermoplastic polyurethane (CTPU) and varioShore TPU (VTPU) during a cyclic test. The flexible sensor can detect deformations in SPAs through the application of air pressure. Similarly, the force sensor exhibits the ability to detect grasping objects by detecting changes in resistance. These findings suggest that the resistance change corresponds directly to the magnitude of the mechanical stimuli applied. Thus, the device shows potential for functioning as a resistive sensor for soft actuation. Furthermore, these findings highlight the significant potential of 3D and 4D printing technology in one-shot fabrication of soft sensor-actuator robotic systems, suggesting promising applications in various fields like grippers with sensors and rehabilitation devices.

Self-sensing metallic material based on piezoelectric particles

Structural parts’ integrity should remain as specified and designed, although it can change due to ageing and use by environmental action and accidental events. Structural parts design is carried out considering certain service conditions that can be different from operating conditions. These parts often experience dynamic solicitations that change in amplitude and frequency over time, which may cause the part’s failure. The part’s real condition can be assessed by Structural Health Monitoring systems, which grant significant social, economic, and environmental impact, as they can reduce maintenance costs and ensure the integrity of the part and its surroundings. This can be performed with an integrated monitoring system comprising sensors, on the surface or embedded, into the components. However, surface sensors are subject to damage, from collisions and/or environmental action, and embedding sensors can be very challenging and may result in a weakened part. A Self-Sensing Material (SSM) was developed based on piezoelectric particles embedded in metal parts by a solid-state processing technology. The material can act as a sensor and continuously monitor its condition. The SSM's generates an electrical voltage signal when subject to strain stimulus. Moreover, the solid-state processing technology employed promotes mechanical properties enhancement in the processed zone, not only by the grain size reduction but also due to the incorporation of the piezoelectric particles. The response to a set of dynamic loads was assessed and was found to be coherent with the solicitations applied.

Dynamics pattern of a radioactive rGO-magnetite-water flowed by a vibrated Riga plate sensor with ramped temperature and concentration

In recent times, the dynamics study of an electrically weak performing fluid stream regulated by Riga sensors has become an emerging research topic for scientists. Riga sensors’ utility for improving the effectiveness of heat and mass transport rates in industrial and engineering systems is diverse. This motivates us to inspect the stream pattern and heat-mass transmission mechanism of an electrically low-performing hybrid nanofluid (rGO-magnetite-water) near a vertically straightened Riga plate sensor embedding with absorbing materials under the guidance of thermal and concentration buoyancy and magnetization. The taken flow is being modelled by incorporating pertinent physical influences, namely radiation heat emission, chemical reaction, and ramped temperature and concentration at the boundary wall. The flow is presented mathematically in terms of unsteady partial differential equations. The compact-form expressions for model entities are founded by opting for the Laplace transform methodology. The Riga plate’s shear stress, heat and mass transfer rates are tabulated and graphed. The physical behaviours of substantial flow entities against model factors are conversed and judged graphically. The vital findings of this study demonstrate a swelling in the velocity distribution with an enhancement in modified Hartmann number and diminishing with an enlargement in the width of electrodes. The temperature and concentration are higher for constant plate temperature (CPT) and lower for ramped plate temperature (RPT). It is also motivating to note down that hybrid nanofluid containing reduced graphene nanomaterials will transmit extra heat in the flow regime. The heat flow across the Riga sensor elevates against the higher radiation parameter’s value. These novel findings will be extremely applicable in steam generators, chemical reactors, hybrid Riga plate electromagnetic devices, and phase transitions during material processing.

Novel Contact Modeling for High Aspect Ratio Soft Robots

Contact modeling between a soft robot and its environment is challenging due to soft robots' compliance and the difficulty of embedding sensors. Current modeling methods are computationally expensive and require highly accurate material characterization to produce useful results. In this article, we present a contact model that utilizes linear complementarity and Hencky bar-chain methods, and requires only static images to efficiently predict the interaction between actuator and environment. These methods have yet to be introduced to the soft robotics community for modeling robots that deform due to eigenstrains or strains not caused by external forces. We validated our model using a custom experimental setup and computer vision algorithm on 3-mm OD, 90-mm long, tube-like actuators. Our results indicated a 1.06% difference in shape between model and experiment, with computation times in 10 s of ms—three to four orders of magnitude faster than nonlinear gradient descent. Additionally, the error in interaction forces between the model and experiment decreased as pressure increased, with an average error magnitude of 45% and 21% for pressures at the low and high ends of the tested range, respectively.