Department of Mechanical Engineering

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

Polymeric gels, when made into thin films, are reported to depict a reversible folding behavior on exposure to favorable solvents as a result of the coupled diffusion–deformation mechanism. In this work, we develop a model based on the coupled diffusion–deformation mechanism which can predict the large deformation caused during this reversible folding behavior of a polymeric thin film. The formulation incorporates a concentration dependent Young's modulus variation. It is found that the incorporation of concentration dependent Young's modulus of the film in the formulation is essential to predict the folding curvatures accurately. The results obtained using this model are validated with the experimental results of the Chitosan–Water system by comparing the folding characteristics — intermediate folding stages and curvature, while accurately explaining the mechanism of reversible folding. The model is applied to other material systems such as PVA–Water and PDMS–Hexane systems. Furthermore, we demonstrate that the developed model successfully predicts the buckling and wrinkling phenomenon observed experimentally in stimuli-responsive polymer thin films. © 2020 Elsevier Ltd

Department of Applied Mechanics

World University root node

Mechanics of Materials

Coupled diffusion–deformation behavior of stimuli-responsive thin polymer films

Literature reveals that machining a silicon wafer is very arduous due to its high brittle nature and low fracture toughness. Researchers have attempted to machine features on a silicon wafer using both conventional and non-conventional processes and both have their merits &amp; demerits. Abrasive jet machining (AJM) is one of the efficient non-conventional machining methods for machining hard and brittle materials. Removal of material occurs by impact erosion and is free from Heat Affected Zone (HAZ) and resolidification. These advantages make AJM a potential process for machining silicon wafers. Therefore an attempt has been made to machine channels on silicon wafers using a Tailor-made AJM setup. The process parameters selected for machining channels on a silicon wafer are air pressure, Feed rate (FR), and Standoff distance (SOD). The output responses considered for characterization are material removal rate (MRR), channel characteristics like width, depth, and Surface Roughness (Ra). The machined Channels are found to have sufficient depth and good surface finish and are free from heat affected zone and resolidification. X-Ray Diffraction (XRD) analysis has indicated the presence of embedded SiC particles in the channel. GRA method is utilized to achieve an optimal condition of higher depth and MRR, smaller width, and Ra. Also, the significance and percentage contribution of process parameters are found out through ANOVA and obtained results are presented in the paper. © 2021, Springer Nature B.V.

Silicon

Machining, Characterization and Optimization: A Novel Approach for Machining Channels on Silicon Wafer Using Tailor-Made Micro Abrasive Jet Machining

Nacre, a composite layer present in sea-shells, exhibits a remarkable combination of toughness, strength, and stiffness through its brick–mortar micro-structure, acting as a template for novel materials. Strength, one of nacre's important properties is highly variable due to distribution of underlying material's properties as well as various defects present in its micro-structure. Presently, researchers assume the Weakest-link hypothesis and consequently use the Weibull distribution to model this variability. However, this assumption is theoretically unproven for biological materials such as Nacre and extrapolation of the same to predict rare but catastrophic behavior would be incorrect. In this article, the suitability of Weibull distribution to account for the variability of strength in nacre is tested, using multi-scale models, developed using FEM. Through Monte Carlo based numerical experiments and Renormalization Group (RG) based arguments, it is shown that the Weakest-link hypothesis, which is commonly used to justify the use of the Weibull distribution, does not seem to hold for nacre. Micromechanics and non-local homogenization based distributions such as the one suggested by Luo and Bazant (2019) might be more appropriate for accurate extrapolation to the low probability tail. © 2021 Elsevier Ltd

Department of Humanities and Social Sciences

Testing Weibull as a viable statistical strength distribution for Nacre

To avoid excessive tissue removal and collateral damage, the high-power density laser is apt for dental surgery also need to have high precision. For high-precision dental surgery with minimal tissue damage, the present work frames a method to predict laser ablation profile based on surface morphology and chemical composition of dentin. The surface morphology and chemical composition were studied on different dentin samples using scanning electron microscope (SEM) and Energy Dispersive X-ray Analysis (EDAX), respectively. The key laser ablation parameters (ω0, Deff, and Fth) were determined by laser irradiation study using 800 nm, Ti:Sapphire femtosecond laser at processing condition of 100 fs, 10 kHz and 10 mm/s. The dentin samples show a strong linear correlation between physiochemical characteristics and laser ablation parameters. The surface morphology exhibits a negative linear correlation with threshold fluence, whereas the converse is true for chemical composition. The laser ablation parameters of a random dentin sample are derived from the knowledge of linearity data. From the obtained laser ablation parameters, the complete theoretical ablation profile is constructed and validated with experimental ablation profile. Even though the surface morphology of dentin shows high linearity, the concentration of Ca and P can be used as the most feasible probe in clinical settings. Furthermore, the laser ablation rate and ablation efficiency are predicted by the method to optimize the laser processing condition for any specific teeth. The versatility of the method overcomes the problem of heterogeneity on various teeth and simplifies the method of finding optimal laser processing condition for immaculate laser surgery. © 2020 Wiley Periodicals LLC.

Journal of Biomedical Materials Research - Part B Applied Biomaterials

Physiochemical characteristics: A robust tool to overcome teeth heterogeneity on predicting laser ablation profile

We propose an outlier detection-based statistical approach to identify and locate a defect in composite plates using far fewer number of sensing points compared to conventional imaging techniques. The key steps involved in this computationally inexpensive approach are the random sparse selection of the sensing points through Poisson disk sampling, followed by a two-step outlier detection process based on thresholding and computation of median absolute deviation. The robustness of the proposed technique is explored through extensive simulations involving different defect sizes, random locations on flat plate structures, and various values of signal to noise ratio (SNR). We experimentally demonstrate the feasibility of detection of delamination, whose size is comparable to the ultrasonic wavelength with probability of detection (PoD) better than 90% using <1% of the total number of samples required for conventional imaging, even under conditions wherein the SNR is as low as 5 dB. © 2021 John Wiley & Sons, Ltd.

Department of Electrical Engineering

Structural Control and Health Monitoring

Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

This paper aims to develop a technique for estimating an aluminium/epoxy/aluminium lap shear joint stiffness. Shear horizontal guided waves are employed using periodic permanent magnet electromagnetic acoustic transducers (PPM EMATs). Based on the source influence of the EMATs and sensitivity of SH-like mode to adhesion, an appropriate transduction regime is selected. Different modality of inspection, such as reflection and transmission mode, is explored. The arrival time of the received signal in transmission mode depends on the adhesion level. A procedure for calculating the energy velocity in the bond and inverse calculation of bond stiffness is proposed. Experiments were performed on samples of different surface treatments. Based on the proposed method, the stiffness of the samples was estimated. Results show the technique's potential to quantify the adhesion level rather than merely classifying as good, weak, and bad bonds. © 2020 Elsevier Ltd

International Journal of Adhesion and Adhesives

Bond stiffness estimation with shear horizontal guided waves generated using PPM-EMATs

For defense applications, rapid X-ray inspection of propellant samples is essential for the identification and assessment of defects. Automation of this process using artificial intelligence is possible by properly training a neural network model. Convolution Neural Networks (CNNs) have recently demonstrated excellent success in both the tasks of image recognition and localisation using an adequate amount of data. In real-world, it’s not an easy task to produce the correct amount of experimental data required for the deep neural network to operate. In this work, we propose a method for producing synthetic radiographic data that is supported by ray tracing based radiographic simulations for the deep learning algorithms to automatically detect anomaly in X-ray images. The simulation results, which are then supplemented by noise extracted from the experimental data, show a good comparison with the measurements. This Simulation assisted Automatic Defect Recognition (Sim-ADR) system simultaneously perform defect detection and defect instance segmentation. The accuracy of the defect detection system is more than 87% on a testing set included 416 images. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Journal of Nondestructive Evaluation

Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks

This study investigates the forming limits of cryo-rolled Ultrafine Grained (UFG) aluminium sheet for loading conditions relevant to sheet metal forming. It is shown that a combination of nano-particle strengthening with an annealing heat treatment enables the production of cryo-rolled UFG aluminium with up to 12 % higher material strength and 20 % higher formability compared with the unreinforced UFG condition. While nano-particle strengthening does not significantly influence the tensile ductility, it reduces the bend fracture limit by over 50 % due to an increased dislocation density and a diffuse sub-grain structure. The level of the forming limit curves (FLD) of all UFG materials was up to 5 times higher than their uniform tensile elongation and hardening index; this is attributed to a high strain-rate sensitivity, which increased with annealing heat treatment. In roll forming, the UFG aluminium showed similar material behaviour and shape defects as observed for conventional sheet metals in previous studies. However, given the reduced bend fracture limits observed with nano-particle addition, this study suggests that roll forming may not be a suitable manufacturing solution for nano-particle strengthened UFG sheet metal. © 2021 Elsevier B.V.

Journal of Materials Processing Technology

Understanding material behaviour of ultrafine-grained aluminium nano-composite sheets with emphasis on stretch and bending deformation

Problems involving material interfaces are challenging, owing to the cumbersome requirements in meshing such as a fine matching mesh on both sides of the interface. Often one does not require very fine meshes on either side of the interface owing to different geometric and material properties. If the interface meshes are finer on either side, the degrees of freedom (DOFs) increases substantially. To address this difficulty, in this work the benefits of polygonal elements constructed based on the cell-based smoothed finite element method (SFEM) are explored and an alternate approach for modelling fracture along cohesive interfaces is introduced. The proposed approach greatly reduces the DOFs in the system by allowing for more nodes along the required interface region, whilst allowing for a coarse mesh elsewhere in an elegant way, without compromising the accuracy compared with the conventional finite element method. The proposed framework enables the interface to be represented independent of the meshes at the interface giving complete freedom on meshing. The robustness, accuracy and the convergence properties are demonstrated with a few numerical tests involving straight, curved and multiple interfaces. © 2020 Elsevier Ltd

Department of Ocean Engineering

Engineering Fracture Mechanics

Cell-based smoothed finite element method for modelling interfacial cracks with non-matching grids

Abstract: The southern peninsular India is characterized by unique climatology with rainfall processes throughout the year from land–ocean contrasts. In addition, the complex terrain induces localized effects causing huge spatial and temporal variability in the observed precipitation. This study aims at evaluating the sensitivity of the high-resolution Weather Research and Forecasting (WRF) model (4 km) to multi-physics parameterizations, 3D variational data assimilation, and domain configuration, in the study domain covering southern peninsular India. Furthermore, the study focusses on the formulation of an ensemble method to improve the simulation of precipitation across seasons. A total of 120 experiments were set up across four crucial rainfall events, of varying spatial extent and duration, dominated by different rainfall generation mechanisms. The assessment of the experiments shows that the model’s cumulus and microphysics schemes have the highest impact on the location, intensity, and spread of the simulated 4-day long Quantitative Precipitation Forecasts (QPFs). Applying cumulus schemes at all domains represented the variability in the QPFs, across space and time, for the precipitation events dominated by convective activity. The cases without cumulus schemes at the convective scale domain (4 km), captured the higher intensity rains during organized cyclonic circulations in the north-east monsoon period. Hence, a 10-member multi-physics ensemble approach including members with and without cumulus parameterization at the fine resolution domain was adopted. The preliminary results demonstrate that the mean from the suggested ensemble approach (n-MPP) performed well in capturing the dynamics of QPFs across the rainfall events, as opposed to a single-member deterministic simulation and mean from larger member conventional multi-physics ensemble approach (c-MPP) without cumulus parameterization at the convective scale. The rank histogram, delta semi-variance plots, and outlier statistics at various lead times clearly showed that the suggested n-MPP was able to capture the high-intensity rainfall, increasing the spread of precipitation forecasts and consequently reducing the occurrence of outliers. Highlights: Evaluated the sensitivity of a high-resolution Numerical Weather Prediction (NWP) model (4 km) with 120 experiments in generating short-term (4-day long) quantitative precipitation forecasts (QPFs).The cumulus and microphysics schemes have the highest impact on the location, intensity, and spread of the simulated rainfall across events dominated by different rainfall generation mechanisms.A 10-member multi-physics ensemble approach including members with and without cumulus parameterization at the fine resolution domain was able to capture the high-intensity rainfall, increasing the spread of precipitation forecasts and consequently reducing the occurrence of outliers. © 2021, Indian Academy of Sciences.

Department Of Civil Engineering

Journal of Earth System Science

A multi-physics ensemble approach for short-term precipitation forecasts at convective permitting scales based on sensitivity experiments over southern parts of peninsular India