Profile
Dr Bastola is a mechanical engineer and material scientist specialised in Additive Manufacturing (AM)/3D Printing. His cutting-edge research focuses on developing innovative solutions for soft robotics, sensors/actuators, energy harvesting, and intelligent solutions for acoustic and vibration. Through the development of various passive and active material systems (those responsive to external stimuli) such as magnetic-field responsive and shape-memory materials, Dr Bastola is working toward creating innovative solution to various environmental challenges. Dr Bastola has gained valuable experience working in renowned institutions, including his PhD at Singapore Centre for 3D Printing (SC3DP), Nanyang Technological University (Singapore), postdoctoral research at Institute of Active Polymers, Helmholtz-Zentrum Hereon (Germany), and at the world-renowned Centre for Additive Manufacturing (CfAM), University of Nottingham (UK).
He has been working in the field of multi-functional, multi-material, and metamaterial systems, and their Additive Manufacturing. His research has been published in prestigious journals including Advanced Materials. Dr Bastola is interested in both fundamental, and translational research.
Area of Expertise
- Additive Manufacturing/3D Printing
- Polymers/Polymer Composites
- Smart Materials and Structure
- Stimuli-Responsive Materials (Magnetic/Electric Field, Heat and Humidity)
- Ink formulation for Additive Manufacturing
- Rheology and Viscoelasticity
Projects
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DSLgene: Data-driven Statistical Learning of generalised mechanics of textile composites (funded by the Royal Society, Project number NIF\R1\241753)
Project Overview In this project, we aim to revolutionise the current artificial intelligence modelling approaches of textile reinforcements by integrating machine learning with the extensive wealth of physics knowledge. This facilitates the development of robust material models of textile reinforcements with unparalleled reliability (in terms of extrapolatability), thereby enabling the prediction of unseen scenarios with…