Research & Projects
Research & Projects
Embedded Files
My current research focuses on optimizing engineering solutions, paving the way for more creative and efficient feature representations of CAD geometries, and using these representations to generate models using different ML-based algorithms, especially for automotive hood frames.
My research interests include:
Engineering Design
Structural/ Design Optimization
Finite Element Analysis
AI/ML in Design Engineering
DESIGN PORTFOLIO
PAST PROJECTS
Here's a small description of the projects I've worked on in the past. The full report or paper written is attached on the left side.
Overview
Overview
Developed a multimodal machine learning framework to rapidly predict the structural performance of automotive hood frames in early design, fusing image, geometric, and parametric data from the CarHoods10K dataset to reduce reliance on time-intensive FEA while enabling faster, more informed design exploration.
Objectives
Objectives
Develop a custom multimodal architecture that combines images, cross-sections, and parametric features to predict hood frame performance.
Benchmark multimodal predictions against a unimodal (image-only) baseline.
Test generalization to hood frame geometries not included in training.
Illustrate how multimodal ML can support simulation-driven conceptual design workflows.
Results
Results
The multimodal model achieved validation errors below 20% for von Mises stress, mass, and directional deflection on CarHoods10K, substantially outperforming the image-only baseline, especially for stress and deflection. When evaluated on entirely new hood frame designs, it produced physically reasonable, trend-consistent predictions, confirming improved robustness and generalization and demonstrating its value for rapid, simulation-assisted design iteration.
Overview
Overview
Explored advanced optimization techniques to reduce surface roughness in nickel electrodeposition through simulation and analysis. Using COMSOL Multiphysics and Taguchi methods, the study examined how current densities, time proportion, and solution conductivity affect surface quality in micromanufacturing applications.
Objectives
Objectives
Minimize surface roughness and variation in deposition height.
Analyze effects of forward/reverse currents, time ratio, and conductivity.
Develop a COMSOL model to optimize electrodeposition parameters.
Improve micromanufacturing efficiency across electronics, automotive, and biomedical industries.
Results
Results
Optimization through simulated annealing identified ideal deposition parameters, achieving a surface roughness reduction (Δy = 0.258) and fewer surface defects. The improved model demonstrated smoother finishes and better uniformity, advancing precision manufacturing through optimized electrodeposition processes.
Overview
Overview
Focused on optimizing the material selection for a fire extinguisher lever to reduce mass, enhance performance, and improve sustainability. The lever’s design ensures reliable actuation and durability under varying environmental conditions.
Objectives
Objectives
Reduce lever mass without compromising strength or function.
Withstand ≥75 lb of actuation force.
Maintain a Young’s modulus ≥65 GPa.
Operate reliably between –65°F and 120°F.
Use materials that are non-corrosive and ergonomic.
Results
Results
After evaluating Titanium Alloys, Silicon Nitride, and CFRP, the study identified 409 Stainless Steel as the most efficient and sustainable material. It offers the best balance of strength, cost, manufacturability, and recyclability, meeting performance goals while supporting eco-friendly design principles. Future research will explore lighter alternatives with comparable durability.
Optimizing the Performance of a 3D-Printed Brake Pedal using Design of Experiments (DOE) and ASTM Standard Testing Methods.
Optimizing the Performance of a 3D-Printed Brake Pedal using Design of Experiments (DOE) and ASTM Standard Testing Methods.
Overview
Overview
Explored 3D printing applications in automotive design by optimizing a brake pedal for strength, durability, and performance. The project examined how printing parameters and materials influence the mechanical behavior of 3D-printed components.
Objectives
Objectives
Analyze tensile, flexural, and impact strength using DOE and ASTM standards.
Evaluate the effects of infill pattern, raster angle, layer thickness, material type, and infill density.
Develop an optimized brake pedal design through generative design and reverse engineering.
Results
Results
The optimized 3D-printed brake pedal showed superior mechanical performance, with infill density and material selection emerging as the most influential factors. Adjusting raster angle and print orientation further enhanced the strength and durability. The final design demonstrates significant potential for advanced braking systems and future additive manufacturing innovations in automotive engineering.
Reinventing Cable Protection: Design and Analysis of a 3D Printed ABS Cable Protector
Reinventing Cable Protection: Design and Analysis of a 3D Printed ABS Cable Protector
Overview
Overview
Developed a 3D-printed ABS protector to improve the durability and lifespan of USB cables by reducing bending, fraying, and handling-related damage. Compared its performance with existing market models through structural and material analyses.
Objectives
Objectives
Design a durable, cost-efficient cable protector using 3D printing and ABS material.
Compare performance and structural strength with existing protectors.
Analyze stress, strain, and load-bearing capacity under real-world conditions.
Results
Results
The newly designed protector showed up to 88% lower stress and 83% less deformation than existing models, withstanding loads up to 100 N. These improvements confirm its superior durability, stability, and cost-effectiveness for everyday USB use.
Page updated
Google Sites
Report abuse