Materials science is an interdisciplinary field dedicated to examining the properties and characteristics of various materials, focusing on enhancing existing engineering materials and developing new ones. Grounded in principles of physics, chemistry, and occasionally biology, this discipline is essential for technological advancement. At the Mohan Lab, we leverage advanced artificial intelligence (AI), machine learning (ML), artificial neural networks (ANN), and large language models (LLM) to study and evaluate a range of materials, including superalloys and metamaterials tailored for additive manufacturing.

A key objective of the Mohan Lab is to use AI, ML, and LLM to perform dynamic mechanical analysis and investigate the physical, metallurgical, and corrosive properties of additively manufactured and hybrid welded materials. This includes various metals, ceramics, metamaterials, superalloys, high-entropy alloys, and dissimilar material compositions. We employ various AI models, including generative design algorithms, reinforcement learning, and predictive modelling, to develop novel materials, including innovative alloys and advanced ceramics. By integrating these technologies, we aim to optimise material performance and manufacturing processes, contributing to sustainable and innovative solutions in the field.

Research in this lab is broadly classified as follows:

1. Hybrid Additive Manufacturing on Nickel-Based Superalloys, High Entropy Alloys (HEAs), and Ceramics Utilising AI and ML for Process Optimization.

2. Dynamic Mechanical Behavior of Hybrid Additive Manufactured Alloys

3. Deliberate Defects to Enhance the Ductility of Additive Manufacturing Superalloys and High Entropy Alloys: An AI-Driven Approach for Characterization and Optimization.

4. Novel Post-Processing Methods for AM Grain Structure Modification

To know more about our research, please check the publications.