Research & Projects

My research focuses on developing AI and machine learning methods for generative engineering design, geometric learning, and data-driven surrogate modeling informed by physics and first principles.

My research interests include:

Generative AI for Engineering Design

Engineering Design is changing all around us. AI/ML methods are transforming the way we look at design. I'm specifically interested in exploring how we can better integrate the use of ML methods within this workflow to assist design engineers, explore design spaces under valid constraints.

Geometric Representations for ML Models

I am interested in exploring how geometric data and engineered features can be effectively represented to machine learning models. This includes developing representations that capture structural and functional aspects of 3D shapes, enabling models to learn design intent and performance relevance. My goal is to identify how different geometric embeddings, topological descriptors, and feature-based parameterizations influence learning efficiency and generalization in AI-driven design.

Data-Driven and Physics-Based Surrogate Modeling

I develop data-driven surrogate models for mechanical and automotive engineering simulations to accelerate complex analyses. My recent work involves creating machine learning surrogates that capture simulation responses efficiently for use in design and optimization workflows. Going forward, I aim to extend these models by incorporating physics-informed principles, building hybrid frameworks that combine empirical data with a first-principles understanding to improve generalization and interpretability.

AI Foundations for Computational Design

I am broadly interested in developing foundational AI frameworks that integrate engineering reasoning into their architecture and training. This includes unifying design generation, evaluation, and optimization under a machine learning paradigm that respects engineering context and domain knowledge.

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.

Multimodal Machine Learning Model for Performance Prediction

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

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

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.

Development & Analysis of B-Spline Curves

Overview

Developed a C++ based interactive software framework for constructing, manipulating, and plotting B-Spline curves, surfaces, and knot operations, leveraging B-Spline basis functions to support mechanical engineering design automation and CAD/CAM applications.

Objectives

Implement B-Spline curve generation using recursive basis functions, knot vectors, and control points for accurate shape representation.
Create an interactive console interface supporting curve (B-Spline, Bezier, Cubic), surface (NURBS, Bezier), and knot operations (insertion, refinement).
Enable visualization and modification of geometric entities to explore effects of parameters like order, knots, and indices.
Address challenges in parameterization, index bounds, and Bezier as B-Spline special cases for design automation.

Results

The software successfully generated and plotted various B-Spline curves (e.g., smooth refinements post-knot insertion), Bezier curves adhering to convex hull properties, NURBS surfaces with weight modifications, and other types like rational ruled surfaces, demonstrating precise control over geometric shapes and continuity while highlighting limitations in plotting smoothness and modularity for future enhancements.

Optimization of Surface Roughness in Electrodeposition Process.

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

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

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.

Material Selection for Lightweight Fire Extinguisher Levers

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

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

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.

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

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

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

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

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

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.

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