Doctorate ( from Department of Mechanical Engineering, National Institute of Technology (NIT) Kurukshetra and presently working as Professor & Head of Department (Mechanical Engineering), Chandigarh University, Chandigarh (since March 2020). Extensive academics and administrative experience prior, with Sharda University (Greater Noida) & U.P Technical University affiliated colleges in Agra as various capacities (COE, HoD and Dean Academics at K.P Technical Campus and Reader in FET RBS College).
Advancements in High Temperature Oxidation of Metals and Alloys Moniya, Jashanpreet Singh, Anuj Kumar Sehgal Aerospace Engineering Materials Applications Modelling and Sustainability, 2026 High-temperature oxidation is an important process that has a large effect on the performance, durability, and reliability of metals and alloys used in advanced engineering applications, like nuclear reactors, power generation systems, aerospace structures, and gas turbines. When metals are heated up, they react more quickly with oxygen and other oxidizing agents. This causes oxide scales to form, which can either protect the metal or speed up its breakdown. The ability of a material to resist oxidation depends on factors like its thermodynamic stability, diffusivity, its microstructural features, and the presence of certain alloying elements. This chapter discusses the basic processes of high-temperature oxidation, how protective oxide films form and work, and general ways to make things more resistant to oxidation. To make materials that can work reliably in harsh, high-temperature environments, you need to have a clear understanding of these ideas.
Coating Methods for Protection against Slurry Erosion Slurry Erosion Flow Phenomenon Complexities and Protection Methods, 2026
Predictive modeling of thermoplastic nanocomposites using machine learning algorithms Harshit Sharma, Gaurav Arora, Papiya Bhowmik, Manoj Kumar Singh, Vinod Ayyappan, Anuj Kumar Sehgal, Sanjay Mavinkere Rangappa, Suchart Siengchin Discover Mechanical Engineering, 2025 In this investigation, various machine learning (ML) algorithms, i.e. Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (kNN) were employed to estimate the mechanical properties of microwave-processed polymer nanocomposites. A universal testing machine evaluated the tensile strength, fracture toughness, and Young’s modulus of Polypropylene/Carbon Nanotubes (PPNT), High-Density Polyethylene/Carbon Nanotubes (HPNT), and Low-Density Polyethylene/Carbon Nanotubes (LPNT) composites. The results indicate that SVR is the most accurate and reliable model for estimating the mechanical properties of all composites. Its superior performance is due to its ability to minimize inaccuracies and generalize effectively, even with limited datasets. Among all models, SVR consistently delivered the best performance for all three mechanical characteristics. It achieved low values for root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) i.e. 0.0228, 0.0189, and 0.0005, respectively along with a high coefficient of determination (R 2 ) of 0.9875. These metrics highlight its low error, excellent generalization, low bias, and consistent performance across all datasets. Despite RF performing better than DT, it underperformed compared to SVR, with RMSE variations of 6.1% for HPNT, 47.3% for PPNT, and 32.4% for LPNT. Meanwhile, kNN proved to be the least suitable algorithm in this study due to its poor predictive accuracy and lack of stability.
