The present article investigates the possibility of using nonlinear resonance ultrasound spectroscopy to determine the acoustic nonlinearity parameter (β) and third order elastic constant by developing an inverse problem. A theoretical framework was developed for nonlinear forced vibration of a cantilever beam using material nonlinearity (stress-strain nonlinearity). The resulting nonlinear equation was solved using method of multiple time scales to obtain the nonlinear frequency shifts. The present works focuses only on classical nonlinearity and, therefore, a diverse group of intact, classic nonlinear materials were chosen. The samples were tested using nonlinear resonance ultrasound spectroscopy, and the developed theory was used to invert the experimental frequency shifts to obtain the nonlinearity parameters. The third order elastic constants and β were calculated using their analytical relationship with the nonlinearity parameter. The experimentally determined C₁₁₁ and β values for all various materials agree well with literature values. In addition to determining β, determination of the sign, or phase of β was also explored theoretically and experimentally.

The testing and evaluation of adhesive bonding quality between thermoplastics are crucial for structural integrity. This article presents the use of phased array ultrasonic testing (PAUT) method to characterise the adhesive interface between thermoplastic composites. Samples with three different bond conditions: control, bad and mid-level were fabricated and tested using PAUT. A damage index (DI) based classification framework aided by machine learning (ML) algorithm is proposed to classify different adhesion conditions. A set of 18 physics-based damage indices were extracted from each PAUT image for quantitative characterisation. ML algorithms were developed to build a non-linear mapping that correlates the input DIs with the output sample types to address the classification problem. The experimental results show that support vector machine (SVM) performs better than other ML algorithms with classification accuracy greater than 95%, and the defined DIs can differentiate among bad, mid-level, and control samples.

In the present work, the interaction of Rayleigh waves with a delamination in a fiber reinforced composite plate was analyzed. Rayleigh waves, upon interacting with delamination mode, convert into Lamb waves in the delamination zone. These guided Lamb modes have the capability to mode convert back into Rayleigh modes when they interact with the edge of the delamination. A unidirectional glass/epoxy laminate with a delamination of known size was fabricated and tested using air-coupled ultrasonics. Finite element models were developed to understand the mode conversions occurring at various sections of the delamination. Particle displacements along with numerical and experimental velocities were considered to identify each mode. Conclusions were drawn based on the velocity analysis.