Engineered Materials and Structures Laboratory (EMSL)
Research
Our research is interdisciplinary, combining engineering design, computation, and fabrication. Our research interests and expertise broadly lie in the field of materials and structures (such as composites, functionally graded materials, multi-material, metals, and alloys) at multiple length-scales involving both forward and inverse problems of engineering that warrants frequent excursions among the boundaries of applied mathematics, physics, probability theory and nanotechnology with an ideal balance between fundamental developments and industry-oriented applications. In product development, it considers early conceptual design phases through to the design and fabrication of novel solutions. Current topics include design heuristics, computational design, design for additive manufacturing and 4D printing. We investigate a wide variety of application areas of mechanical and structural systems across a number of industries including consumer products, robotics, space, biomedical, civil, mechanical, machine design and aerospace.
News & Events
Dr. Kishor Balasaheb Shingare
Post-doctoral Fellow
His work on predicting the effective properties in composites got media coverage at IIT Bombay
Dr. Susmita Naskar
Principle Investigator
She received Young Engineer Award By The Institution of Engineers
Dr. Kishor Balasaheb Shingare
Post-doctoral Fellow
Best paper award with Dr. Naskar on 35th Indian Congress for The Institution of Engineers
Dr. Susmita Naskar
Principal Investigator
Dr. Naskar and her collaborator's work on metamaterial featured and released in media
Latest Publications
Probing the Stochastic Dynamics of Coronaviruses
A machine learning assisted efficient, yet comprehensive characterization of the dynamics of coronaviruses, in conjunction with finite element approach, is presented. Without affecting the accuracy of prediction in low‐frequency vibration analysis, an equivalent model for the FE analysis is proposed, based on which the natural frequencies corresponding to first three non‐rigid modes are analyzed. Results from this first of its kind study on coronaviruses along with the proposed generic machine learning based approach will accelerate the detection of viruses and create efficient pathways toward future inventions leading to cure and containment in the field of virology