Numerical Investigation of Excitation of Various Lamb Waves Modes in Thin Plastic Films

Ultrasonic-guided waves are widely used for the non-destructive testing and material characterization of plates and thin films. In the case of thin plastic polyvinyl chloride (PVC), films up to 3.2 MHz with only two Lamb wave modes, antisymmetrical A0 and symmetrical S0, may propagate. At frequencies lower that 240 kHz, the velocity of the A0 mode becomes slower than the ultrasonic velocity in air which makes excitation and reception of such mode complicated. For excitation of both modes, we propose instead a single air-coupled ultrasonic transducer to use linear air-coupled arrays, which can be electronically readjusted to optimally excite and receive the A0 and S0 guided wave modes. The objective of this article was the numerical investigation of feasibility to excite different types of ultrasonic-guided waves, such as S0 and A0 modes in thin plastic films with the same electronically readjusted linear phased array. Three-dimensional and two-dimensional simulations of A0 and S0 Lamb wave modes using a single ultrasonic transducer and a linear phased array were performed. The obtained results clearly demonstrate feasibility to excite efficiently different guided wave modes in thin plastic films with readjusted phased array.

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.

Nonlinear Inversion of Ultrasonic Guided Waves for in Vivo Evaluation of Cortical Bone Properties

Ultrasonic guided waves (UGW), which propagate throughout the whole thickness of cortical bone, are attractive in the early diagnosis of osteoporosis. However, it is challenging due to the impact of soft tissue and the inherent difficulties related to the multiparametric inversion of cortical bone quality factors, such as cortical thickness and bulk wave velocities. Therefore, an UGW based multiple-parameter inversion algorithm is developed to predict strength-related factors in this research. In simulation, a free plate (cortical bone) and a bilayer plate (soft tissue and cortical bone) are used to validate the proposed method. The inverted cortical thickness (CTh), longitudinal velocity (V L ) and transverse velocity (V T ) are in accordance with the true value. Then four bovine cortical bone plates are used in the in vitro experiments. Compared with the reference values, the relative errors for cortical thicknesses are 3.96%, 0.83%, 2.87% and 4.25% respectively. In the in vivo measurements, ultrasonic guided waves are collected from ten volunteers’ tibia. The theoretical dispersion curves depicted by the estimated parameters (V T , V L , CTh) match well with the extracted experimental ones. In comparison to the dual-energy x-ray absorptiometry (DXA), the results show that the estimated transverse velocity and cortical thickness are highly sensitive to the osteoporosis. Therefore, these two parameters (CTh and V T ) of long bones have potential to diagnose bone status in clinical applications.