About Me
Background
Research and more Collaboration
Research Interests
Akshansh completed his Bachelor' of Technology in Mechanical Engineering from SRM Institute of Science and Technology, Chennai, India. During his bachelor's thesis, he developed an interest in the solid-state joining process called Friction Stir Welding and in Artificial Neural Networks. He is working as a Data Scientist at International
Akshansh completed his Bachelor' of Technology in Mechanical Engineering from SRM Institute of Science and Technology, Chennai, India. During his bachelor's thesis, he developed an interest in the solid-state joining process called Friction Stir Welding and in Artificial Neural Networks. He is working as a Data Scientist at International Centre for Diffraction Data, in Rome. He pursued his Master's in Materials Engineering and Nanotechnology at Politecnico Di Milano. He did his Master's thesis under the supervision of Professor Sara Bagherifard and Mr. Ramin Yousefi Nooraie at Archès Lab.
Outside academics, he likes to travel, cook, read novels, self-learn new things, and play table tennis. He also likes to learn new languages and currently learning Italian, Telugu, and Tamil.
Research Interests
Research and more Collaboration
Research Interests
He works on the application of Artificial Intelligence-based algorithms in the Manufacturing and Materials sectors. His main research interests are Cognitive Computing, Advanced Manufacturing, Explainable Artificial Intelligence (XAI), Machine Learning, Natural Language Processing, Nature based optimization algorithms, and Computational Neuroscience.
Research and more Collaboration
Research and more Collaboration
Research and more Collaboration
He would love to collaborate on projects that have integration of the Artificial Intelligence algorithms into Materials Science and Manufacturing processes. He can be contacted via mail at akshansh@aifablab.com .
arXiv Profile
Google Scholar Profile
Research and more Collaboration
Google Scholar Profile
Google Scholar Profile
Google Scholar Profile
AI Fab Lab
Cold Spray Data: An Open Source Database
Computational Materials Research Group
AI Fab Lab is a pioneering research company that's bringing the power of artificial intelligence to some of the most important fields out there. We're a group of curious minds and problem-solvers who are passionate about using cutting-edge AI tech to tackle real-world challenges in material science, manufacturing, and healthcare.
Computational Materials Research Group
Cold Spray Data: An Open Source Database
Computational Materials Research Group
Computational Materials Research Group is a self-funded Research and Development Group part of AI Fab Lab. The main objective of this group is to inculcate the application of Artificial Intelligence-based algorithms in Materials and Manufacturing development. We are going to change the course of data-driven material science and manufactur
Computational Materials Research Group is a self-funded Research and Development Group part of AI Fab Lab. The main objective of this group is to inculcate the application of Artificial Intelligence-based algorithms in Materials and Manufacturing development. We are going to change the course of data-driven material science and manufacturing, we assure you that.
Cold Spray Data: An Open Source Database
Cold Spray Data: An Open Source Database
Cold Spray Data: An Open Source Database
Cold Spray Data is a curated repository of experimental and simulation data on cold spray, which is a solid-state deposition process in which metallic or composite particles are accelerated to supersonic velocities and bonded to a substrate upon impact without melting.
The database aggregates data from laboratory experiments, numerical si
Cold Spray Data is a curated repository of experimental and simulation data on cold spray, which is a solid-state deposition process in which metallic or composite particles are accelerated to supersonic velocities and bonded to a substrate upon impact without melting.
The database aggregates data from laboratory experiments, numerical simulations (e.g., FEM, SPH, MD), and literature sources, capturing a wide range of process parameters (gas pressure, temperature, nozzle geometry, standoff distance), feedstock properties (powder morphology, particle size distribution, material composition), and outcome metrics (deposition efficiency, porosity, adhesion strength, hardness, residual stress).