publications

2026

1. Microstructure and Mechanical Properties of Friction Stir Welded AA6082 Aluminium Alloy and Dual-Phase 780 Steel Dissimilar Joints

   Deepak Kumar, Ranjit Bauri, and Tej Prakash

   Journal of Manufacturing Processes, 2026

   Under review

   Abs

   Dissimilar welding of steel and aluminium alloys has attracted widespread attention in the automobile industry to make lighter automobile parts for better fuel efficiency and reduced emissions. Friction stir welding (FSW) is a solid-state welding process that can overcome fusion welding issues. The present work focuses on dissimilar FSW of aluminium alloy (AA6082-T6) and dual-phase steel (DP780) in lap configuration. Different process parameters were considered to establish the process parameter window based on microstructural analysis and mechanical property evaluation.

2. Physics-Informed, Uncertainty-Aware GPR Surrogate Modelling for Friction Stir Welding of Dissimilar Materials

   Deepak Kumar, Ranjit Bauri, and Tej Prakash

   2026

   Under preparation

2025

1. Microstructural Evolution and Corrosion Kinetics of X2 Zr-Nb Alloy in Simulated PWR and BWR Environments

   Rajat Nama, Deepak Kumar, Xingzhong Liang, and 5 more authors

   ASTM STP Proceedings, 2025

   Accepted for publication

   Abs

   This study investigates how dissolved oxygen concentration affects aqueous corrosion of X2 Zr-Nb alloy in nuclear environments. Samples exposed to simulated PWR and BWR conditions for 90 days showed that the BWR environment produced 20% thicker oxides following different growth kinetics. TEM and spectroscopy revealed quantifiable microstructural differences in oxide phase stability, defect formation, and growth kinetics relevant to predicting materials behaviour in varied reactor environments.

2. PoroDet: A Python Package for Nano-porosity Detection in TEM Images

   Rajat Nama, Deepak Kumar, Tanmoy Choudhury, and 2 more authors

   2025

   Submitted to Journal of Open Source Software (JOSS)

   Abs DOI

   PoroDet is an open-source Python workflow for supervised feature extraction from TEM images. The software provides a task-specific end-to-end pipeline integrating dataset preparation, model training, batch inference, and quantitative post-processing. Originally developed for nanopores in Fresnel-contrast TEM images, the workflow is adaptable to other TEM feature classes through user-supplied annotations. It trains a U-Net encoder-decoder CNN, supporting training from scratch or via fine-tuning.