Journal of Intelligent Material Systems and Structures

Recently, magnetorheological elastomers have become interesting smart materials with many new designs for robotics. Various applications have been built with magnetorheological elastomers, such as vibration absorbers, actuators, or valves, showing that this material is promising for industry applications. The objective of this study is to demonstrate that magnetorheological elastomers supported by a permanent magnet can create a soft gripper suitable for handling small and delicate objects such as fruits or candies. The novel concept of the gripper is proposed, exploring the features of a magnetorheological elastomer and a permanent magnet. This gripper uses the energy of a permanent magnet to provide a self-closing gripping mechanism. At the same time, the use of flexible material—elastomer allows it to hold delicate objects of various shapes. The effect of wrapping magnetorheological elastomer around a permanent magnet, the design process, and the characteristics of a soft gripper are presented. The effectiveness of the soft gripper was validated in a series of experiments that involved lifting various objects.

This study primarily investigates the internal friction (IF) behaviour of Ti₅₀Ni₄₈Cu₂ (TNC2) shape memory alloy (SMA) through temperature, frequency, isothermal, and strain sweep analysis using a Dynamic mechanical analyser (DMA). The total internal friction (IF_Total) value of TNC2 alloy is 0.0686 at 323.4 K. The frequency sweep analysis reveals that increasing the frequency reduces the IF_Total by 15.97% due to restricted martensitic interface movement. Isothermal analysis shows a significant drop in IF_Total during the B2-B19′ transformation peak, with reductions of 79.02%, stabilising as intrinsic damping dominates. Strain sweep tests demonstrate that higher strain at isothermal temperature (323.4K) increases inherent internal friction (IF_PT + IF_Int)_B2–B19′, with a frequency-dependent trend. The secondary focus is decomposing the IF_Total into its intrinsic (IF_Int) and inherent internal friction (IF_PT + IF_Int)_B2–B19′, components using an iterative method. The iterative method effectively separates IF_Total, allowing precise calculation of the transformed volume fraction n(T). After eight iterations, convergence is achieved with a minimal error rate. The predicted IF spectrum closely matches experimental data, highlighting the method’s reliability for decomposing IF_Total into its contributions.

Stiffened panels are widely used in a range of engineering domains to enhance load-bearing capacity, reduce buckling, and improve overall structural integrity. Stiffener disbonding is a common failure mode, making it essential to detect these disbonds before they reach a critical level that could lead to structural failure. In this work, a transformer architecture has been implemented to detect the presence and extent of disbonding between the stiffener and the host aluminium plate. The research uses a dataset of Lamb waves obtained from an experimental setup with four types of specimens, ranging from no disbonding to disbonding of varying widths induced by Teflon strips. The problem is framed as a multi-class classification task with labels representing different disbonding levels. The transformer model utilizes a self-attention mechanism to analyze the Lamb wave responses and ascertain the presence and extent of disbanding. Operating in encoder-only mode, the tailored transformer accepts Lamb wave responses as input and outputs a class probability score for each of the four predefined disbonding levels using a softmax activation function. The developed model has been able to predict the state of the stiffened panels with an accuracy of 100%. To demonstrate the effectiveness of the transformer model, it has been compared to the convolutional neural network (CNN) model. When tested on an unseen noisy dataset, the transformer model achieved an accuracy of 100%, outperforming the CNN model, which achieved an accuracy of 89%.

In this study, wave propagation in a submerged sandwich plate with a negative Poisson’s ratio (NPR) core, was investigated. The sandwich plate consists of three parts: two functionally graded piezoelectric material (FGPM) skins at the top and bottom and an NPR core in the middle. Using Hamilton’s principle, the equations of motion for wave propagation in the FGPM sandwich plate with an NPR core were derived, and the dispersion relations were obtained. The effects of the geometric parameters, gradient index, fluid density and depth, wave number, and NPR core parameters on the wave frequency and the phase velocity were analyzed. Results show that presence of fluid leads to a reduction in the phase velocity of the sandwich plate. The results will be useful in the design of structures submerged in fluid environment.