Balaji Elangovan

One of the practical components that can be found in aircraft engines, the Delaval nozzle, works to improve the aircraft's performance in terms of its stability and its capacity to manoeuvre at different speeds of operation. This research concentrates on the acoustic behaviors shown by this nozzle during the transition from low rate to sonic speed. The modification of the nozzle tip at various degrees of inclination on the transition velocity is the procedure used to minimize the amount of acoustic energy emitted. The acoustic behavior is analyzed based on the FW-H acoustics model utilized, and the optimization problem is solved using the practical eddy simulation approach in CFD. The acoustic Power, measured in dB, is estimated at various angles on the nozzle tip, and the performance of the Delaval nozzle is used to show how the redesigned nozzle tip affects the nozzle's acoustic behavior.

Magnesium matrix composites are being used in more and more different ways, so they need to have good surface properties like resistance to wear and corrosion. In this work, we used friction stir processing to make ZE43 magnesium surface composites with different amounts of Si3N4 particles (by volumes 6.0%, 12.0%, and 18.0%, respectively). X-ray diffraction and scanning electron microscope analysis showed Si3N4 particles in the developed magnesium surface composite are all uniformly distributed. After FSP, the composites were tested for hardness. Using Potentio dynamic polarization, the corrosion behavior of both the base matrix material and composites made were studied. The composite containing 18% Si3N4 has the highest corrosion resistance .The composite’s sliding wear behavior and coefficient of friction analyzed by a pin-on-disc tribometer by changing the amount of Si3N4 in the material, load and sliding distance. To attain the highest possible wear resistance and coefficient of friction, parameters of the process were optimized with the help of taguchi grey relational analysis. The results show that load, followed by volumetric percentage, and sliding distance have a substantial effect on wear rate and friction coefficient.

Among many other sectors, the automobile industry relies heavily on resistance spot welding (RSW) to attach metal components. In order to determine if spot welded connections are weldable, this research develops a machine learning (ML) model and a parameter-less optimization method, and then suggests a data-driven welding analytics framework. It trains the ML model using data from experiments and simulations, which allows it to make accurate predictions about weld quality. In order to get the finest possible weld in the shortest amount of time, RL has been used to adjust factors like the FSW pin’s rotational and translational speeds. Due to their thorough exploration of the environment, these algorithms achieved extremely high accuracy rates, indicating that they are trustworthy in their ability to operate the agent optimally. However, there may be a large disparity in the precision with which different models provide predictions. Prioritizing the evaluation and selection of the most effective prediction model or models is of the highest significance. In addition, we build, test, and evaluate previous models of nugget width prediction using deep reinforcement learning (DRL). To begin with, we use bootstrapping to create sample distributions for all of the prediction models. Then, we evaluate their performances using statistical tests to see if there are any significant differences. This investigation shows that DRL which is built for RSW nugget width forecasting in this work, performs better than earlier models.