Gangadharan Raju

Recent studies show that cylindrical shells under axial compression buckle into a fully localized, single-dimple mode, which then further evolves into a periodic post-buckling shape. Predicting where the single dimple initiates and for what reason remains an open challenge. This paper analyses the experimental pre-buckling responses of compressed cylindrical shells to uncover the mechanics that govern the dimple initiation site. Both three-dimensional-printed and metallic shells are studied, and their full-field responses are acquired through a multi-three-dimensional digital image correlation set-up. First, the classical Fourier series approach is used to decompose the pre-buckling deformation field of a three-dimensional-printed shell. While the most dominant mode revealed in this decomposition accurately predicts the circumferential wavenumber of the post-buckling periodic mode shape, it fails to isolate the single dimple. Consequently, we propose a new method based on the full-field bending energy distributions in the pre-buckling regime to pinpoint the single-dimple initiation site. The methodology is applied to multiple shells and successfully reveals the presence of a developing localized mode in the pre-buckling regime at approximately 60% of the critical buckling load. Thus, the proposed methodology will help identify suitable locations for lateral probing to estimate the buckling load of cylindrical shells.

The acoustic emission (AE) technique is a widely used nondestructive method for in-situ health monitoring of composite structures. Unlike metals, failure mechanisms in composite structures are complex, involving multiple damage modes, and each damage mode has a distinct AE signature. This work uses deep learning algorithms called convolutional autoencoder (CAE) and convolutional neural network (CNN) to classify damage modes in carbon fiber-reinforced polymer laminates using AE waveforms. Tensile experiments are carried out on laminates of various stacking sequences, and the acquired raw AE waveforms are transformed into time-frequency planes called spectrograms using short-time Fourier transform. CAE is used for retrieving deep features associated with damage modes in the latent space from these spectrograms. Subsequently, k -means is used to cluster the deep features in the latent space. Each cluster is labeled with a damage mode by inspecting their damage signatures using the scalograms. This labeled data is then used to train the CNN. The CNN, once trained is used on the AE data of pristine and notched quasi-isotropic specimens, and its ability to classify and identify the damage modes is investigated. The trained CNN achieves satisfactory classification accuracy of 96.9% on pristine quasi-isotropic specimen data and 96.4% on notched quasi-isotropic specimen data. When compared to the prediction accuracy of pure damage modes, the prediction accuracy of mixed-mode damage is slightly lower, at 92.5% for pristine and 91.3% for notched quasi-isotropic specimens. This reduction in accuracy is due to the spectrograms of mixed-mode damage containing energy distributed across multiple frequency bands. By classifying the AE waveforms of both pristine and notched quasi-isotropic specimens, the progression of different damage modes is analyzed through the cumulative count of AE waveforms associated with each damage type. These findings enhance the understanding of damage mode evolution in composite structures and contribute to structural health monitoring studies.

The buckling instability of cylindrical shells under axial compression has been one of the most renowned problems in structural engineering for several decades. Many pioneering works in the twentieth century have provided insights into understanding the shells’ infamous imperfection sensitivity and led to reliability-based designs. However, a recent surge in numerical studies of the snaking phenomenon explores the development of a localized stable post-buckling mode in axially compressed cylindrical shells. Hitherto, none of the experimental studies report on the evolution of azimuthal snaking. In this work, experimental studies are carried out with the objective of revealing the snaking phenomenon. The axial compression experiments are performed on 3D-printed shells made of thermoplastic polyurethane (TPU). The work’s novelty lies in the usage of TPU shells for slowing down the propagation of circumferential dimples and making it feasible to capture them using photogrammetry. Despite the match between the experimental and numerical mode shapes, the experiments reveal multiple routes for the snaking sequence. Furthermore, mode transitions such as reduction in circumferential wave number and transformation of symmetric mode into an asymmetric one are observed. These experimental results provide insights into the localized phenomenon of snaking and validate numerical solutions.

Advanced ultrasonic array–based imaging techniques that rely on A-scan data acquired through individual parallel reception of array elements and subsequent post-processing have shown promising performance by generating high-resolution images. An advanced array-based imaging algorithm called sectorial plane wave imaging (SPWI) has been recently proposed with multimode imaging and rapid inspection capabilities. In this work, the efficiency of the SPWI algorithm is demonstrated and compared to the total focusing method (TFM) and plane wave imaging (PWI) algorithms in detecting different types of common weld defects: lack of sidewall fusion, slag, porosity, toe crack, centerline crack, and root crack. The full matrix capture data acquired over each weld defect using a 32-element linear array on an angular wedge configuration is converted to the PWI matrix data to perform SPWI. The corner and indirect mode SPWI inspection results are able to detect all the weld defects with image quality comparable to that of TFM and PWI, but at a higher processing speed due to the slight compromise over reception focusing, making SPWI a viable advanced ultrasonic array–based imaging technique for inspecting welds.

The rapid advancements in the areas of biosensors, soft robotics, and tissue engineering increased the demand for hydrogels (gels) and their composites. This is aided by the ease of tailoring their properties. Consequently, insights into the physical, chemical, mechanical and thermal behaviour of gels become critical. While these insights are traditionally obtained using conventional mechanical tests, performing these becomes difficult owing to the low stiffness and slippery nature of gels. The former is typical of materials like rubber and foam, while the latter is specific to gels. Therefore, reliable non-destructive evaluation (NDE) methods became important to address these difficulties. A range of NDE techniques, based on different physical and mechanical principles, are currently available. This article summarises the established and upcoming NDE techniques to encourage novel research in NDE from a soft materials perspective. It reviews the use of techniques like visual inspection, ultrasonic testing, digital image correlation, optical coherence tomography, infrared thermography, vibration testing, terahertz testing and shearography for the mechanical evaluation of gels. The hardware, working principles, advantages, and disadvantages, and the major areas of application are discussed for each technique. Future directions with regards to conjugated deployment of techniques, interfacing with machine learning, and real-time monitoring capabilities.