Rakesh Balamurugan
Hi!👋 My name is
RAKESH BALAMURUGAN
I am a Mechanical Engineer with a passion for solving multi-scale multi-physics problems using computational methods and experimental methods. My expertise in advanced numerical methods such as Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Thermal Analysis and Fluid Structural Interaction (FSI), combined with knowledge in Machine Learning, Deep Learning and Computer Vision, fuels my passion for creating innovative solutions for complex problems.
My current research interest focuses on fatigue and fractures of materials under various loads, including thermal loading, and developing a data-driven computational model (algorithm) for designing, optimizing topology, optimizing manufacturing methods, and predicting failures of the system. I am also interested in probabilistic computational mechanics and multi-scale uncertainty quantification and propagation. As a dedicated problem solver, I enjoy collaborating with others to deliver high-quality results that meet the needs of modern engineering problems.
SKILLS

ANSYS

AutoCAD

PTC Creo

CATIA

Hypermesh

SolidWorks

ABAQUS

Finite Element Analysis

Computational Fluid Dynamics

Fluid Structural Interaction

Turbulence Modeling

Uncertainty Quantification

Uncertainty Propogation

Probabilistic Computational Mechanics

Python

Matlab

Machine Learning

Deep Learning

Additive Manufacturing

Fatigue and Fracture

RESEARCH & PROJECTS
Turbulence Model for Backward-stepping face
Turbulence Model for Backward-stepping face
Prognostic Analysis of Bulk Metallic Glass-based Cardiac Stent
Prognostic Analysis of Bulk Metallic Glass-based Cardiac Stent
ML and DL approach in low steel alloy mechanical properties estimation
ML and DL approach in low steel alloy mechanical properties estimation
AI-enabled Interactive Threats Detection using
AI-enabled Interactive Threats Detection using
Machine Learning for Predicting Crack Initiation Sites in Additively Manfactured Titanium Alloy
Machine Learning for Predicting Crack Initiation Sites in Additively Manfactured Titanium Alloy
Computational Analysis of Various Flow
Computational Analysis of Various Flow
WORK EXPERIENCE
Research Serive Aide
Arizona State University
09/2022 - Present
  • Organized and facilitated a seminar series on Advanced Air Mobility, with an average attendance of 40 per session.
  • Maintained and updated the ASU NASA ULI and ASU CCSS websites.
Teaching Aide
Arizona State University
09/2022 - Present
  • Taught fundamental concepts and experiments of physics to undergraduate students and graded lab reports of 100 students.
Graduate Service Assistant
Arizona State University
08/2022 - 12/2022
  • Graded Homework, Quizzes, and Assignments 45 of Graduate Students and maintained fairness.
Graduate Research Volunteer
  • Conducted a parametric study on the X65 gas pipeline with interactive corrosion threats using elastic-plastic FEA models; produced more accurate results than ASME B31G.
  • Estimated burst pressure of X65 pipe with actual inner circumferential surface obtained through AI-enabled multi-camera stereo vision system; predicted burst pressure has less than 3% error from experimental results.
  • Processed micro-CT images of additively manufactured titanium alloy with filtering, segmentation, and despeckle, producing high-quality STL files with gas pores, lack of fusions, and surface roughness at the micro-scale level.
  • Implemented a graph-based algorithm to extract the 1D surface roughness and pores from the STL files to find crack initiation sites using Physics Informed Neural Networks.
Founder
Raki Traders
07/2020 - 08/2021
  • Administrated all organization operations, such as making policies, managing inventory, and controlling prices leading to increased returns of 1.1 million per month.
  • Hired, trained, and led a sales team of two for digital marketing and gained 300 new clients in a fiscal year.
LETS TALK
If you liked my previous research work and want to collaborate, then drop me a message!

Made by Arit Chanda & Rakesh