Department of Applied Mechanics

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Synthetic signals that represent fatiguing contractions of biceps brachii muscle are generated in this work using a comprehensive mathematical model. These signals are the biomarkers of muscle electrical activity that could be recorded non-invasively on the skin surface using Surface Electromyography (sEMG). The important components of the adopted synthetic sEMG model are current source, volume conductor, motor unit recruitment, and firing behavior functions. For this study, the amplitude (A) and scaling factor (λ) of the current source function is selected appropriate to fatiguing conditions. Further, tunable Q-wavelet method is applied to compute the frequency range associated with fatigue in the synthetic signal. The resultant wavelet coefficients are obtained using multirate filter bank where the scaling factors α and β are chosen so as to meet the anticipated Q-factor and the ranges of frequency bands. The results show that synthetically generated signal is able to truly represent fatiguing and nonfatiguing conditions. The amplitude-based features of tunable Q-wavelet coefficients are able to identify the characteristic changes associated with varied fatiguing conditions. Model generated frequency responses in fatiguing conditions are in agreement with the experimental results reported elsewhere. As fatigue is a temporary failure of skeletal muscles to maintain a required force for the accomplishment of a particular task, the model proposed here could be used as a validation of sEMG measurements in health and disease. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

Lecture Notes in Mechanical Engineering

Model-Based Simulation of Surface Electromyography Signals and Its Analysis Under Fatiguing Conditions Using Tunable Wavelets

In this work, an attempt has been made to classify emotional states using Electrodermal Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA signals are obtained from the publicly available DEAP database and are decomposed into tonic and phasic components. The phasic component is subjected to the short-time Fourier transform. Thirty-eight features of time, frequency, and time–frequency domain are extracted from the phasic signal. These extracted features are applied to CNN to learn robust and prominent features. Five machine learning algorithms, namely linear discriminant analysis, multilayer perceptron, support vector machine, decision tree, and extreme learning machine are used for the classification. The results show that the proposed approach is able to classify the emotional states using arousal-valence dimensions. Classification using CNN learned features are found to be better than the conventional features. The trained end-to-end CNN model is found to be accurate (F-measure&nbsp;=&nbsp;79.30% and 71.41% for arousal and valence dimensions) in classifying various emotional states. The proposed method is found to be robust in handling the dynamic variation of EDA signals for different emotional states. The results show that the proposed approach outperformed most of the state-of-the-art methods. Thus, it appears that the proposed method could be beneficial in analyzing various emotional states in both normal and clinical conditions.

Department Of Chemical Engineering

Expert Systems with Applications

Convolutional neural network based emotion classification using electrodermal activity signals and time-frequency features

When a Unidirectional fiber reinforced polymer (UDFRP) composite is subjected to transverse loading, there is spatial variation of stresses in the constituents. The failure in matrix initiates at the location of maximum stress. Stress distribution and failure initiation in constituents of UDFRP composites is usually studied through finite element (FE) analysis of representative volume element (RVE) which is computationally expensive and time consuming. The present study proposes an analytical model through which stress variation and failure initiation in the constituents of UDFRP composite can be obtained in simple and reliable way and it can be readily used in designing. For this model, RVE is idealized in the form of springs arranged in parallel and series. These springs represent the stiffness of constituents (fiber and matrix). The results of analytical model are compared with FE simulations and good agreement is observed. Influence of fiber volume fraction on failure initiation of UDFRP composites is also studied through FE analysis of RVE and analytical model. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Department of Engineering Design

Annals of Solid and Structural Mechanics

Micromechanics based analytical model for estimation of stress distribution and failure initiation in constituents of UDFRP composites subjected to transverse loading

The Lamb wave time-reversal method has been widely proposed as a baseline-free method for damage detection in thin-walled structures. Under varying thermal environments, it would require that the time reversibility of Lamb waves is temperature invariant. In this study, we examine the temperature dependence of Lamb waves and its time reversibility using experiments and finite element simulations on isotropic plates with surface-bonded piezoelectric wafer transducers for actuation and sensing. The study is conducted at three different temperatures of the system from 25°C to 75°C for a wide range of excitation frequency. The results indicate that the time reversibility can undergo significant changes due to temperature variations depending on the excitation frequency. However, at the best reconstruction frequency corresponding to the maximum similarity of the reconstructed signal with the original input signal (proposed recently as the probing frequency), the change in the percent similarity with temperature is insignificant. The results also demonstrate that changes in the physical properties of both adhesive layers and piezoelectric transducers with temperature play a dominant role in influencing Lamb wave amplitudes. However, only the change in the characteristics of the adhesive layers is responsible for the temperature dependence of the time reversibility of Lamb waves.

Fulltext

Structural Health Monitoring

Time reversibility of Lamb waves in thin plates with surface-bonded piezoelectric transducers is temperature invariant at the best reconstruction frequency

Sensors and Actuators B: Chemical

<a href="https://www.sciencedirect.com/science/article/pii/S092540052030808X">U-bent fiber optic plasmonic biosensor platform for ultrasensitive analyte detection</a>

Damage detection in complicated engineering systems from vibration measurements typically involves the use of algorithms that are built on the principles of bayesian dynamic state estimation. These methods invariably required the solution of the forward problem a fairly large number of times. For complex engineering systems that are numerically modeled using Finite Element (FE), this can be computationally intensive especially when a single FE run for the problem takes a large time. To alleviate this problem, there is a need for the development of Reduced Order Models (ROMs) that significantly reduce the computational cost associated with solving the forward problem, for a given measure without sacrificing the accuracy. The present study discusses three ROM methods with specific reference to a simple problem. These methods include well-known Component Mode Synthesis (CMS) and System Equivalent Reduction Expansion Process (SEREP) which are applicable only for linear systems, as well as Principal Component Analysis (PCA)—the method which is more general and can be used for nonlinear systems as well. A comparison of the performance of all these methods is carried out for a stepped beam. The FE based results obtained from the full model is treated as the benchmark. © 2021, Springer Nature Singapore Pte Ltd.

Analysis of Stepped Beam Using Reduced Order Models

This study focusses on the vibration control of linear large scale engineering structures, modelled as continuous system, with varying system parameters. Because of manufacturing limitations or/and measurement errors, system parameters need to be modelled as random variables. Here, system parameters are modelled as non-Gaussian random variables. Mathematically, continuous system is governed by partial differential equations and solved using approximation methods. High fidelity finite element model is the starting point of the analysis. Since numerical approximation involves large number of degrees-of-freedom, solving the system in real time is computationally expensive and application of control algorithm is cumbersome. In this study, a reduced order model is developed to reduce the state space dimension of the problem and further integrated with the control algorithm. Next, controller gain is obtained using linear quadratic regulator in reduced subspace which is used as a feedback to the actuators to produce the required control force for vibration control. A numerical example of flexible cantilever beam is solved to demonstrate the efficacy of the algorithm and probabilistic characterisation is carried out using Monte Carlo simulation. © 2020 The Society of Instrument and Control Engineers - SICE.

2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020

ROM integrated vibration control of stochastically parametered flexible structures

Magnetostrictive materials that couple mechanical and magnetic domains have been widely explored for use in sensors and actuators. These materials often exhibit a nonlinear material response under applied magnetic fields, which limits the use of linear constitutive models. Furthermore, the nonlinear constitutive relations tend to be implicit in nature. Hence, a finite element scheme that can handle the implicit relationship between the mechanical (stresses and strains) and magnetic (magnetic flux density and magnetic field) quantities is proposed in order to arrive at solutions to boundary value problems. In the proposed scheme, while the physical requirements of equilibrium and strain–displacement relation are satisfied point-wise, the constitutive relations hold in a weak integral sense. A fully coupled magnetostrictive plane stress rectangular element is developed based on the proposed scheme and its efficacy in arriving at solutions to coupled field boundary value problems is illustrated by subjecting the element to standard loading conditions. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Department Of Civil Engineering

Computational Mechanics

Finite element formulation for implicit magnetostrictive constitutive relations

Magnetostrictive materials are potential candidates for many applications such as sensors, actuators, transducers, and other magnetoelectric applications. Cobalt ferrite (CoFe 2 O 4) has proven to be favorable in comparison with commonly used magnetostrictive materials due to its high magnetostriction coefficient and low cost. This work deals with the synthesis of CoFe 2 O 4 and subsequent characterization of its magnetostrictive properties. Hydrothermal route was adopted for the synthesis, and the effect of sintering and measurement temperatures on the magnetostrictive response of the synthesized samples was also established. Bipolar magnetostriction has been observed in pure CoFe 2 O 4 for the first time, and its control by means of the sintering temperature has been elucidated. The results thus reveal that the temperature is an important parameter in determining the magnetostrictive characteristics of CoFe 2 O 4. The switching from bipolar to unipolar magnetostriction under elevated sintering and measurement temperatures was also observed, and this was owed to the crystal anisotropy of the material. The effect of prestress on the magnetostriction was also studied, wherein it was observed that the application of a compressive prestress resulted in broadening of the magnetostriction loops. The reported bipolar magnetostrictive characteristics are quite interesting and hence can prove to be cost-effective in comparison with existing magnetostrictive materials. © 2020 Author(s).

Journal of Applied Physics

Bipolar magnetostriction in CoFe2O4: Effect of sintering, measurement temperature, and prestress

This manuscript discusses a magnetically coupled nonlinear hybrid piezo-electromagnetic energy harvester under harmonic base motion. Linear energy harvester works optimally when the natural frequency of the harvester meets the resonating frequency, elsewhere harvested power falls drastically. Most of the ambient vibration sources are random in nature. Hence, considering the realistic application, narrowband linear vibration energy harvesters are inefficient. Alternatively, nonlinear energy harvesters are capable of producing the electrical power over a broad frequency range. Hence, to acquire the optimum power in the broader frequency range, a magnetically coupled hybrid piezo-electromagnetic energy harvester is developed. In this current work, a tip loaded unimorph piezo cantilever beam configuration is used to scavenge electrical energy from the strain developed in the piezoelectric patch and spring-magnetic mass attached to another end of the cantilever beam with solenoid arrangements are used to scavenge electrical energy from the relative motion between magnetic mass and solenoid. This hybrid harvester is coupled with magnetic oscillators to introduces nonlinearity in the developed harvester. This paper compares, the energy harvested from the hybrid harvester with that of conventional piezoelectric and electromagnetic harvesters for both linear and nonlinear systems. © Springer Nature Singapore Pte Ltd 2020.