A severely damaged knee due to arthritis or injury require replacement to relieve pain and perform normal activities.
The correct positioning of the resection blocks is of great importance for post-surgery success of TKA.
The already available alignment devices for TKA are either too expensive or too complicated for real time application.
The objective of the is project is to Develop a cost effective and user-friendly surgical device for the correct positioning of the cutting guides during TKA.
The device is supposed to provide real-time quantitative measurements of varus/valgus femoral flexion and tibial slope angles, and resection depth with minimum incision.
In this project, a consistent set of methodologies will be developed for progressive damage modeling of composite laminates for aircraft tail wing under flight loading conditions.
The progressive damage model will include the delamination and debonding damage (interlaminar failure) as well as the ply failure (intralaminar failure) response of the composite laminates.
Develop probability of detection model for composite aircraft tail wing using SLAP(Stochastic Life Approach).
Delamination is one of the most critical defect in laminated composite as it can propagate rapidly and may cause catastrophic failure.
Contrary to high-frequency guided waves and acoustic emission, our research group specializes in the local and global assessment of delamination using low-frequency structural vibration response.
Our research group has successfully implemented Principal Component Analysis (PCA), conventional machine learning (e.g., Support Vector Machine (SVM)) and deep learning (e.g., Convolutional Neural Network (CNN) ) for the autonomous assessment of delamination in smart composite laminates.
Currently, we are working on transfer learning and physics-based data augmentation for prognostic and health management of lab scale and real life composite structures.
Development of physical model of Turbine and Boiler system
Acquisition of the real plant operating data
Data augmentation of real operating data by using physical model
Usage of Artificial Intelligence(AI) for the prognostics and health management(PHM) of power plants
Key words : Artificial Intelligence, Physical model, Data driven approach, Data augmentation, Prognostics and health management, Power plants
Material and piezoelectric characterization of Electro-Active Paper actuator, MEMS & NEMS device, bacteria cellulose application
Thermo-electro-mechanical multidisciplinary analysis, vibration/noise suppression and active control, piezoelectric shunting, damage modeling, structural health monitoring, integrated smart structures and distributed sensors, ultrasonic applications
Static and dynamic analysis of composite and smart composite structures, rotating machines and turbomachinery, dovetail joint in gas turbine engine
Strength and layup optimization of composite laminates, optimal design of smart panel, performance optimization of smart structures
Computational analysis using commercial FEM packages(ABAQUS, NASTRAN, ANSYS), geometric modeling in finite element analysis, numerical algorithms used in large scale structural analysis and nonlinear analysis
High fidelity analysis of composite plates and shells, nonlinear response of composite structures (aerospace applications), damage modeling and characterization, adaptive composites
Fretting fatigue, vibration measurement using laser vibrometry, damage characterization, manufacturing composite specimens (including delamination) and smart composite specimens, material characterization of EAPap, modal test