Recent Tool Condition Monitoring (TCM) approaches aim to optimise tool replacement by reducing unutilised Remaining Useful Life (RUL) and preventing unexpected failures of Diamond-Coated Burrs (DCBs). Acoustic Emission (AE) has been established as an indirect monitoring method for DCB wear through precision tool measurements. This study assesses the impact of changing initial runout on both wear progression and AE-based monitoring. Twelve wear tests were conducted using an adjustable tool-holder to vary initial runout between 1–78µm, with AE and frequent on-machine surface measurements collected throughout. Results show that increased initial runout negatively affects total tool life and introduces greater variability in wear behaviour under identical conditions. Using a Renishaw NC4+ Blue (NC4) system, a high frequency of measurements enabled tracking of each DCB’s progression through the three wear phases. Surface crater formation and circumferential wear band expansion were consistently observed in the final wear phase of all DCBs. AE proved effective in monitoring both runout severity and the wear progression of the tool’s high spot. AE features also identified key wear points throughout the tool’s life, regardless of initial runout. These findings highlight the critical influence of runout on small-diameter DCB performance and support AE as a reliable, indirect, on-machine sensing method for tool wear monitoring, capable of identifying varying initial tool conditions.

Acoustic emission (AE)-based fault diagnosis in structural health monitoring (SHM) systems faces challenges of data scarcity and model overfitting due to the complexity of AE data acquisition and the high cost of labeling. To address these issues, this study systematically explores various data augmentation techniques for AE signal processing and evaluates their impact on model robustness and accuracy. Furthermore, given the complexity of traditional machine learning (ML) models and their deployment challenges on resource-constrained embedded devices, we investigate lightweight ML algorithms and propose a Tiny ML (TinyML)-based fault diagnosis approach. Experimental validation on a carbon fiber panel fault diagnosis case demonstrates that the proposed method significantly improves classification performance under data-scarce conditions while enabling real-time fault diagnosis on embedded systems. These findings underscore the potential of integrating data augmentation, lightweight ML algorithms, and TinyML to enhance both diagnostic accuracy and real-time performance in SHM applications.

The increased focus on predictive maintenance of safety-critical engineering structures requires an onboard structural health monitoring system, which is reliable and robust to provide accurate predictions of health metrics of structures while also being efficient and streamlined to facilitate autonomous data processing and real-time decision-making capabilities. An onboard structural health monitoring system with the capability to continuously monitor and interrogate a structure, describe its current state, and assess the operational risks of the degraded structure needs to be developed and matured so that it can be deployed in practical, real-time monitoring scenarios. This would constitute a cyberphysical system in structural health monitoring. A cyberphysical system is a mechanism that is controlled by computer-based algorithms integrated with the Internet and working with users. There exists a physical domain that is under examination and its digital counterpart, which is informed by data from the physical as well as simulation models. While there exist multiple surveys on the overarching advantages, limitations, and potential of realizing a cyberphysical system, innovation on structural systems, in-line signal processing, and damage event detection in the context of a cyberphysical system, especially from an experimental point of view is still in its infancy. In this work, we implement a versatile cyberphysical framework—CyberSHM using a sparse network of transducers and an edge computing device. Hosted on the structure of interest, the transducers possess the capability to interrogate the structure continuously, periodically, on-demand or autonomously when triggered by damage or an unplanned acoustic event. In addition, the device also possesses efficient on-edge feature extraction and signal classification capabilities, which serve as crucial starting points for further damage analysis and characterization on the digital layer.

Previous research on the potentially damaging effects of vibration on cultural heritage items has largely focussed on earthquakes, road and rail traffic, and heavy construction activities. Comparatively little attention has been paid to vibration caused by sound, such as music, but with museums relying increasingly on income from events such as weddings and concerts, this cause of vibration is of growing concern in a heritage context. Anecdotal observation and some publications have reported objects 'wandering' on glass shelves, bumping into each other, and uneasiness about fragile objects potentially being excited by vibration, and consequently being damaged. As part of this study, a data acquisition system was developed which captured both sound and vibrations simultaneously. Sound reaching a display case was analysed and the potential for sound-induced vibrations to be transmitted to heritage items inside the display case was evaluated. It was found that low frequency sound below 200 Hz was the cause of most of the vibration observed. Greater sound levels (louder music) did not necessarily cause greater vibration levels. One way of mitigating potentially negative effects of sound during events in museums is not necessarily to limit sound levels, but to dampen the museum display cases and filter damaging frequencies from music being played.

BACKGROUND:Orthopedic surgeons refine their torque sensitive skills from tightening cancellous screws. Still, experienced surgeons exhibit surprisingly high screw stripping rates in osteopenic cancellous bone. Whether Acoustic-Emission technology, detecting energy waves from microstructural damage during screw purchase, can reduce these rates is unclear. Our aim was to evaluate if surgeons, irrespective of their experience, reduced cancellous screw stripping rate by combining their skills with feedback from an innovative Acoustic-Emission screwdriver. METHODS:Thirteen orthopedic surgeons with 0-23 years´ experience inserted 468 large fragment cancellous screws through plates into synthetic osteoporotic bone. The 1st stage, surgeons tightened 9 screws each without Acoustic-Emission feedback. The 2nd stage, each tightened 18 screws using the Acoustic-Emission feedback modified screwdriver. The last stage, surgeons tightened 9 screws each, again without Acoustic-Emission feedback. A strain gauge on the screwdriver was used to verify screw stripping. FINDINGS:Surgeons stripped 36 out of 115 screws (31 %) in stage 1, 37 out of 227 screws (16 %) in stage 2, and 26 out of 114 screws (23 %) in stage 3. A significant reduced screw stripping rate was found in stage 2 compared to in stage 1 (p < 0.001). Neither the individual surgeon nor experience of the surgeon contributed to screw stripping probability in a mixed effect logistical regression model. INTERPRETATIONS:Acoustic-Emission technology is superior to the torque sensitive skills of surgeons, demonstrating its potential to assist surgeons in real time, regardless of their experience, in reducing screw stripping rates in cancellous bone.

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs) for grinding high strength ceramics or glass, in which the random nature of the tool, inconsistent manufacturing methods and high wear rates create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool's life being underutilised due to premature replacement. Workpiece surface damage, increased grinding forces and large-scale diamond grain pullout could all be the result of high levels of runout and in-circularity common within electroplated DCBs. As such it is important to not only monitor the overall tool wear but also tool condition. Acoustic Emission (AE) presents as an indirect on-machine sensing method highly suited to grinding applications. The high frequency range of AE, >20 kHz, prevents machine noise from dominating the acquired signals, isolating the micro-scale machining processes within noises machine environments. AE resulting from the grinding process has the potential to monitor not only tool wear but also runout and circularity. A series of DCB wear tests have been conducted, each consisting of the continuous acquisition of AE during grinding and frequent tool surface measurements. Preliminary results demonstrate AE kurtosis can be seen as an indicator of each tool’s runout, representing the fraction of time the tool and workpiece are in contact during a revolution. As a result, an indirect monitoring system capable of monitoring wear and tool state with AE could be utilised within the manufacturing sector.

Composite structures' complex, anisotropic nature poses challenges for damage monitoring. Non-intrusive inspection methods are crucial for continuous monitoring, aiding the shift to predictive maintenance. Onboard monitoring systems must efficiently acquire and analyze signals, correlating them with damage metrics for automated assessment of structural integrity and optimal maintenance planning. Real-time monitoring faces hurdles in handling large data volumes due to computing constraints. This paper proposes a cyber-physical architecture for acquiring and classifying ultrasonic guided wave signals. It employs a sparse transducer array and an edge device for signal processing. Experiments involved exciting a 12-layer carbon fiber composite panel with tone-burst sinusoidal signals.The responses to these excitations were captured and subjected to soft-threshold-based wavelet denoising to extract the structural acoustic response in the ultrasonic frequency band. Subsequently, the conditioned signals were transformed into time-frequency scalograms, which were then employed to train a multi-class classification algorithm operating on the edge device for effective real-time signal classification in an in-service setting.

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs), in which the random nature of the tool and inconsistent manufacturing methods create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool’s life being underutilised due to premature replacement. Acoustic Emission (AE) in conjunction with Machine Learning (ML) models presents a possible on-machine monitoring technique which could be used as a prediction method for DCB wear. Four wear life tests were conducted with a ∅ 1.3 mm #1000 DCB until failure, in which AE was continuously acquired during grinding passes, followed by surface measurements of the DCB. Three ML model architectures were trained on AE features to predict DCB mean radius, an indicator of overall tool wear. All architectures showed potential of learning from the dataset, with Long Short-Term Memory (LSTM) models performing the best, resulting in prediction error of MSE = 0.559 μ m ^2 after optimisation. Additionally, links between AE kurtosis and the tool’s run-out/form error were identified during an initial review of the data, showing potential for future work to focus on grinding effectiveness as well as overall wear. This paper has shown that AE contains sufficient information to enable on-machine monitoring of DCBs during the grinding process. ML models have been shown to be sufficiently precise in predicting overall DCB wear and have the potential of interpreting grinding condition.

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

Damage or flaws in aeronautical structures compromise performance and safety; therefore, it is very important to identify approaches that can be used for damage detection. In this paper, a composite laminated plate with bonded aluminium stiffeners, which is part of a representative wing structure, is analysed using the finite element method. Debonding is assumed to exist in the connection between the plate and one stiffener, an area found to be critical in previous numerical and experimental static analyses, and several dynamic analyses are carried out. First, it is found that the modes of vibration are only marginally affected by the debonds considered. Then loads that vary harmonically in time are applied to the stiffened panel and contact restrictions between damaged surfaces are imposed. Time histories, phase plane plots, frequency spectra and Poincaré sections of the response in diverse points are extracted. It is verified that these non-linear dynamics based tools allow for the detection of detachments in the connection between stiffeners and composite laminated plates. The response is analysed at several points to see if any are better suited to detect detachment. Different excitation frequencies are attempted and it is shown that excitation at half a resonance frequency has advantages.

Microneedle (MN) array patches present a promising new approach for the minimally invasive delivery of therapeutics and vaccines. However, ensuring reproducible insertion of MNs into the skin is challenging. The spacing and arrangement of MNs in an array are critical determinants of skin penetration and the mechanical integrity of the MNs. In this work, the finite element method was used to model the effect of MN spacing on needle reaction force and skin strain during the indentation phase prior to skin penetration. Spacings smaller than 2-3 mm (depending on variables, e.g., skin stretch) were found to significantly increase these parameters.

Porosity is a major manufacturing defect which affects the matrix dominate properties of continuous fibre composites, in particular the transverse strength. The simulation of porosity allows for predictions on the reduction of strength to be made, however, there is a trade-off between accuracy and computational efficiency. The multi-scale modelling approach presented here allows for accurate 3-dimensional geometry data of voids to be used. This is accomplished by first evaluating the effect the porosity has on degrading the matrix then subsequently, by using a representative unit cell, ply level strength can be predicted. The model is validated against empirical tensile and compressive testing of unidirectional autoclave cured prepreg with strong correlation. The approach allows for a reduction in overengineered structures by predicting accurate material properties for a given porosity generation.

The use of three piezoelectric sensors in a small array has previously been investigated to assess its ability to locate Acoustic Emission (AE) sources in simple aluminium and composite structures. It has been theorised that this approach could be used within wireless systems to monitor AE in aircraft, as the close spacing of the sensors removes the need for power-intensive time synchronisation between nodes and reduces excessive cabling. There are, however, two major limitations to this, primarily that the localisation techniques used are only viable within simple structures and additionally that the high computational requirements of the continuous wavelet transform which the technique utilises is infeasible for a low power system. This paper presents modifications to this approach, including the novel second differential method for single sensor modal analysis, which enables the technique to locate AE events within complex structures. A mapping technique is also presented, which accounts for complexity and varying wave velocity in composite structures. Testing is presented which uses artificial sources on a range of structures, from simple plates to an Airbus A350 aircraft wing, with a high level of accuracy and repeatability shown.

The use of CFRP composites is significantly increasing in the aerospace, automotive, and marine industries, particularly in safety critical primary structures. This work presents a newly developed experimental approach to investigate the directional diffusion of water in CFRP composites with the use of Fick's law. The approach is used to study the effect of fiber architecture on directional diffusion rates, with a particular focus on the role of fiber waviness in the diffusion process. A comparison of water diffusion is made in three different fiber architectures: Unidirectional (UD), plain weave, and twill weave. The specimens were fully immersed in 90 degrees C purified water until their maximum moisture saturation was achieved, with some specimens being selectively exposed from the edges only to obtain the directional diffusion coefficients. The water penetration process into the CFRP structure initiate from the micro-cracks and defects. The experimental work of this study shows sharp mass increases within the first stage followed by an equilibrium stage where saturation is present. The interfacial region is found to be a critical parameter where detachment of the interfacial fiber/matrix bonding is observed further demonstrating the potential effect of different fiber architecture in this region. UD fiber architecture showed similar to 20% higher diffusion coefficient in the D-x,D-y direction compared with plain and twill woven architectures. The weave patterns in 2D woven fiber architectures are therefore believed to play a key role on the moisture ingress mechanism and subsequently contributed in slowing down the capillary process in the interfacial region. This has implications for materials development and selection for CFRP composites used in moist environments.

The development of both commercial and research driven wireless Acoustic Emission (AE) devices has increased in recent years. These have the potential to substantially improve the ease at which AE can be monitored, and so reduce the cost of doing so. Monitoring AE wirelessly has significant differences to other Structural Health Monitoring (SHM) applications; this chapter gives some key challenges faced and an overview of available systems. The development and testing of a state-of-the-art, low power, device for AE monitoring of bridges is also presented. Testing showed this device to be capable of continuous monitoring of a bridge structure, utilizing photovoltaic energy harvesting and novel power saving approaches.

There is a continuously increasing demand for non-invasive damage detection methods in aerospace certification testing. This demand is driven by increasing cost effectiveness of these tests by reducing down-time for inspection. This paper aims to evaluate the usefulness of using an audible acoustics camera to detect and locate damage within a relatively large structure under bending load. An acoustic camera is a microphone array of known dimensions, which uses a ‘delay-and-sum’ beamforming numerical model, can detect sources of sound. Under the assumption that damage produces sound, this tool should aid in detecting damage. This method of damage detection was validated against strain gauge, LVDT data, and video recording showing the damage propagation. This paper finds acoustic cameras to have a potential to become a stand-alone detection tool. The authors suggest potential developments to the existing system which could potentially produce a more comprehensive method.

The need for fast and effective Non-Destructive Testing (NDT) techniques is ever present. Existing techniques such as ultrasonic testing, whilst established and reliable, face many limitations when considering large structures such as those found in the aerospace and green energy sectors. Wave mode, as well as other wavenumber based filtering techniques have been presented to address many of these limitations. This work describes a novel application of Wave Mode Spectroscopy (WMS) along with feature detection for complex geometric shapes. The specimen's geometry is found during the wavefields measurement through the use of a 3D Scanning Laser Doppler Vibrometer (SLDV) allowing the wavefield to be mapped to a 2D plane with limited distortion of the wavelength and without any prior knowledge of the part's geometry. This was shown to allow WMS to be applied to continuous, multi-frequency wavefields and generate accurate thickness maps. Monogenic signal analysis has been applied to the same measurement data to generate amplitude maps that allow the automatic detection of edge features through the use of a Canny edge detection algorithm.

Mechanical vibrations from heavy machines, building structures, or the human body can be harvested and directly converted into electrical energy. In this paper, the potential to effectively harvest mechanical vibrations and locally generate electrical energy using a novel piezoelectric-rubber composite structure is explored. Piezoelectric lead zirconate titanate is bonded to silicone rubber to form a cylindrical composite-like energy harvesting device which has the potential to structurally dampen high acceleration forces and generate electrical power. The device was experimentally load tested and an advanced dynamic model was verified against experimental data. While an experimental output power of 57 μ W cm −3 was obtained, the advanced model further optimises the device geometry. The proposed energy harvesting device generates sufficient electrical power for structural health monitoring and remote sensing applications, while also providing structural damping for low frequency mechanical vibrations.

Reducing the noise and improving the sound quality of vehicles’ interior space is one of the challenges to enhance passengers’ experience. This is an ever-growing issue as entirely electric cars are becoming commonplace, making previously unnoticed noise a significant problem. Heating, Ventilation and Air Conditioning (HVAC) units are a major noise source in a vehicle’s interior space, yet automotive manufacturers only give a maximum dB specification to HVAC unit manufactures. Problematic noise is only typically identified once the unit is within the vehicle at the late stages of a project. Psychoacoustics is the study of human perception to sound, allowing unpleasant noise to be identified within recorded data. Within this study, an industrial prototype HVAC unit was analysed using a 96-channel acoustic camera capable of isolating and locating noise sources from the unit using beamforming. In addition to identifying the location of noise sources, several psychoacoustic metrics were used, such as sharpness and loudness, to identify undesirable noise within an extensive data set due to the vast range of test configurations. Testing was conducted to analyse the unit. Within the initial testing, an ‘annoying’ sound was identified at a particular motor RPM, and this was located using the camera to an area which indicated that it was a result of structural resonance. In addition, present was a high-frequency source which could not be located accurately. The results of this testing enable modifications to the unit to be made early in its’ development, either structurally to alter the resonance of the unit or within the settings to ensure certain RPMs are avoided.