Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning.

Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.

Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models.

Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.

Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.

The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.

Total hip replacement monitoring: numerical models for the acoustic emission technique.

Any mechanical instability associated with total hip replacement (THR) excites elastic waves with different frequencies and propagates through the surrounding biological layers. Using the acoustic emission (AE) technique as a THR monitoring tool provides valuable information on structural degradations associated with these implants. However, several factors can compromise the reliability of the signals detected by AE sensors, such as attenuation of the detected signal due to the presence of biological layers in the human body between prosthesis (THR) and AE sensor. The main objective of this study is to develop a numerical model of THR that evaluates the impact of biological layer thicknesses on AE signal propagation. Adipose tissue thickness, which varies the most between patients, was modeled at two different thicknesses 40 mm and 70 mm, while the muscle and skin thicknesses were kept to a constant value. The proposed models were tested at different micromotions of 2 µm, 15-20 µm at modular junctions, and different frequencies of 10-60 kHz. Attenuation of signal is observed to be more with an increase in the selected boundary conditions along with an increase in distance the signals propagate through. Thereby, the numerical observations drawn on each interface helped to simulate the effect of tissue thicknesses and their impact on the attenuation of elastic wave propagation to the AE receiver sensor.

Controlling the thickness dependence of torsional wave mode in pipe-like structures with the gradient-index phononic crystal lens.

The fundamental torsional wave mode, T(0,1), is typically preferred in monitoring defects in long-range pipe-like structures due to non-dispersive and low attenuation characteristics such that the wave packet is not distorted with distance. However, the sensitivity of T(0,1) wave mode depends on the defect type and orientation. While it is highly sensitive to cracks along the axis of pipes, the sensitivity to detect thickness change is low as T(0,1) wave mode generates tangential displacement motion around the pipe circumference. In this study, a gradient-index phononic crystal (GRIN-PC) lens is integrated into a steel pipe to manipulate its dispersion characteristics such that the phase velocity of T(0,1) wave mode is affected by the overall change in pipe thickness. The modified behavior of T(0,1) wave mode within the GRIN-PC lens region increases the damage detection capability of T(0,1) wave mode. Numerical models involving a parametric unit cell study indicate that the phase velocity of T(0,1) wave mode decreases as the thickness of the pipe with the GRIN-PC lens decreases, in contrast with the conventional pipe. The signal difference coefficient (SDC) is applied to the acquired signal from the focal point of GRIN-PC lens to quantify the uniform thickness change. Full-scale wave propagation simulations involving solid mechanics coupled with the piezoelectric actuation demonstrate that the SDC increases as the thickness decreases. The numerical results are validated with experiments using three steel pipes with different wall thicknesses.

Enhancing Acoustic Emission Characteristics in Pipe-Like Structures with Gradient-Index Phononic Crystal Lens.

Phononic crystals have the ability to manipulate the propagation of elastic waves in solids by generating unique dispersion characteristics. They can modify the conventional behavior of wave spreading in isotropic materials, known as attenuation, which negatively influences the ability of acoustic emission method to detect active defects in long-range, pipe-like structures. In this study, pipe geometry is reconfigured by adding gradient-index (GRIN) phononic crystal lens to improve the propagation distance of waves released by active defects such as crack growth and leak. The sensing element is designed to form a ring around the pipe circumference to capture the plane wave with the improved amplitude. The GRIN lens is designed by a special gradient-index profile with varying height stubs adhesively bonded to the pipe surface. The performance of GRIN lens for improving the amplitude of localized sources is demonstrated with finite element numerical model using multiphysics software. Experiments are conducted using pencil lead break simulating crack growth, as well as an orifice with pressured pipe simulating leak. The amplitude of the burst-type signal approximately doubles on average, validating the numerical findings. Hence, the axial distance between sensors can be increased proportionally in the passive sensing of defects in pipe-like geometries.

Prediction of tribocorrosion processes in titanium-based dental implants using acoustic emission technique: Initial outcome.

The use of dental implants is growing rapidly for the last few decades and Ti-based dental implants are a commonly used prosthetic structure in dentistry. Recently, the combined effect of corrosion and wear, called tribocorrosion, is considered as a major driving process in the early failure of dental implants. However, no previous study has reported the prediction of tribocorrosion processes in advance. Therefore, this study is a novel investigation on how the acoustic emission (AE) technique can predict tribocorrosion processes in commercially-pure titanium (cpTi) and titanium-zirconium (TiZr) alloys. In this study, tribocorrosion tests were performed under potentiostatic conditions and AE detection system associated with it captures AE data. Current evolution and friction coefficient data obtained from the potentiostatic evaluations were compared with AE absolute energy showcased the same data interpretation of tribocorrosion characteristics. Other AE data such as duration, count, and amplitude, matched more closely with other potentiostatic corrosion evaluations and delivered more promising results in the detection of tribocorrosion. Hence, AE can be consider as a tool for predicting tribocorrosion in dental implants. Experimental results also reveal Ti5Zr as one of the most appropriate dental implant materials while exposing Ti10Zr's lower effectiveness to withstand in the simulated oral environment.

Non-invasive early detection of failure modes in total hip replacements (THR) via acoustic emission (AE).

Total hip replacements (THR) are becoming an common orthopedic surgucal procedure in the United States (332 K/year in 2017) to relieve pain and improve the mobility of those that are affected by osteoarthritis, ankylosing spondylitis, or injury. However, complications like tribocorrosion, or material degradation due to friction and corrosion, may result in THR failure. Unfortunately, few strategies to non-invasively diagnose early-stage complications are reported in literature, leading to implant complications being detected after irreversible damage. Therefore, the main objective of this study proposes the utilization of acoustic emission (AE) to continuously monitor implant materials, CoCrMo and Ti6Al4V, and identify degradations formed during cycles of sleeping, standing, and walking by correlating them to potential and friction coefficient behavior. AE activity detected from the study correlates with the friction coefficient and open-circuit potential observed during recreated in-vitro standing, walking, and sleeping cycles. It was found that the absolute energy level obtained from AE increased as the friction coefficient increased, potential decreased, and wear volume loss increased. Through the results, higher friction coefficient and AE activity were observed in Ti6Al4V alloys while there was also a significant drop in potential, indicating increased tribocorrosion activity. Therefore, AE can be utilized to predict material degradations as a non-invasive method based on the severity of abnormality of the absolute energy and hits emitted. The correlation between potential, friction coefficient, and AE activity was further confirmed through profilometry which showed more material degradation in Ti6Al4V than CoCrMo. Through these evaluations, it was demonstrated that AE could be utilized to identify the deformations and failure modes of implant materials caused by tribocorrosion.

Hip implant performance prediction by acoustic emission techniques: a review.

Nowadays, acoustic emission (AE) has its applications in various areas, including mechanical, civil, underwater acoustics, and biomedical engineering. It is a non-destructive evaluation (NDE) and a non-intrusive method to detect active damage mechanisms such as crack growth, delamination, and processes such as friction, continuous wear, etc. The application of AE in orthopedics, especially in hip implant monitoring, is an emerging research field. This article presents a thorough literature review associated with the implementation of acoustic emission as a diagnostic tool for total hip replacement (THR) implants. Structural health monitoring of an implant via acoustic emission and vibration analysis is an evolving research area in the field of biomedical engineering. A review of the literature reveals a lack of reliable, non-invasive, and non-traumatic early warning methods to evaluate implant loosening that can help to identify patients at risk for osteolysis prior to implant failure. Developing an intelligent acoustic emission technique with excellent condition monitoring capabilities will be an achievement of great importance that fills the gaps or drawbacks associated with osteolysis/implant failure. Graphical abstract.

Characterization of GMAW (Gas Metal Arc Welding) Penetration Using Ultrasonics.

Welding defects such as lack of penetration, undercutting, crater crack, burn-through and porosity can occur during manufacturing. Assessing weld quality using nondestructive evaluation methods is important for the quality assurance of welded parts. In this paper, the measurement of weld penetration, which is directly related to weld integrity, is investigated by means of ultrasonics. Both linear and nonlinear ultrasonic methods are studied to assess their sensitivities to weld penetration. Welded plates with different penetration depths controlled by changing weld heat input are manufactured using gas metal arc welding (GMAW). Microscopic properties are assessed after the ultrasonic measurements are completed. Numerical models are built using the weld profile obtained from macrographs to explain the relationship between linear ultrasonic and weld penetration. A quantitative correlation between weld morphology (shape, width and depth) and the energy of linear ultrasonic signal is determined, where the increase of weld bead penetration exceeding the plate thickness results in decrease of the energy of the ultrasonic signal. Minimum detectable weld morphology using linear ultrasonics is defined depending on the selected frequency. Microhardness measurement is conducted to explain the sensitivity of nonlinear ultrasonics to both weld penetration and heterogeneity in weld. The numerical and experimental results show that the weld geometry influences the ultrasonic measurement other than the materials' properties.

Combined Damage Index to Detect Plastic Deformation in Metals Using Acoustic Emission and Nonlinear Ultrasonics.

Understanding the amount of degradation using nondestructive evaluation (NDE) methods provides an effective way of determining the fitness to service and the residual life of structural components. Due to uncertainties introduced by the single NDE method, a combined damage index using multi-sensor data increases the reliability of damage assessment. In this paper, the outputs of three NDE methods including acoustic emission (AE), linear ultrasonics (LUT), and nonlinear ultrasonics (NLUT) are merged to identify the amount of plastic deformation in aluminum 1100. The sensitivities of individual and combined methods to microstructural changes are evaluated. The coupon samples are loaded up to different strain levels and then unloaded. AE data is recorded in real time and ultrasonic data is recorded from the unloaded samples. The major features combined in the damage index are cumulative AE absolute energy and nonlinear coefficient. The microstructural state is verified with microscopic analysis and hardness testing. The developed damage index can nondestructively assess the amount of plastic deformation with higher reliability.

The Detection of Burn-Through Weld Defects Using Noncontact Ultrasonics.

Nearly all manufactured products in the metal industry involve welding. The detection and correction of defects during welding improve the product reliability and quality, and prevent unexpected failures. Nonintrusive process control is critical for avoiding these defects. This paper investigates the detection of burn-through damage using noncontact, air-coupled ultrasonics, which can be adapted to the immediate and in-situ inspection of welded samples. The burn-through leads to a larger volume of degraded weld zone, providing a resistance path for the wave to travel which results in lower velocity, energy ratio, and amplitude. Wave energy dispersion occurs due to the increase of weld burn-through resulting in higher wave attenuation. Weld sample micrographs are used to validate the ultrasonic results.

Acoustic Emission Signal of Lactococcus lactis before and after Inhibition with NaN 3 and Infection with Bacteriophage c2.

The detection of acoustic emission (AE) from Lactococcus lactis, ssp lactis is reported in which emission intensities are used to follow and define metabolic activity during growth in nutrient broths. Optical density (OD) data were also acquired during L. lactis growth at 32°C and provided insight into the timing of the AE signals relative to the lag, logarithmic, and stationary growth phases of the bacteria. The inclusion of a metabolic inhibitor, NaN3, into the nutrient broth eliminated bacteria metabolic activity according to the OD data, the absence of which was confirmed using AE data acquisition. The OD and AE data were also acquired before and after the addition of Bacteriophage c2 in L. lactis containing nutrient broths during the early or middle logarithmic phase; c2 phage m.o.i. (Multiplicity of infection) was varied to help differentiate whether the detected AE was from bacteria cells during lysis or from the c2 phage during genome injection into the cells. It is proposed that AE measurements using piezoelectric sensors are sensitive enough to detect bacteria at the amount near 10(4) cfu/mL, to provide real time data on bacteria metabolic activity and to dynamically monitor phage infection of cells.