Our Projects

High‑Accuracy Thickness Coatings

To produce consistent coating thickness on dipped workpieces, we designed a system that combines dual robots, optical micrometers and a heat tunnel. Real‑time measurements enable dynamic adjustment of dipping parameters, delivering precise and repeatable results.

This automated solution improves quality control and production efficiency by eliminating manual measurement errors and maintaining optimal process conditions.

US Patent Application No. 19/262,799 – Dip Coating a Working Wire for a Biological Sensor (filed July 8, 2025)

Production Rate Improvement with Gantry Robot

Manual transfer of workpieces between baths was slow and error‑prone. We implemented a gantry robot that automates the movement of parts through multiple process stages, optimizing the sequence for maximum throughput.

The robotic solution dramatically increased production rate and reduced mistakes, allowing operators to focus on higher‑value tasks.

US Patent Application No. 19/264,592 – Calibration for a Biological Sensor (filed July 9, 2025)

Customized Scheduling Machine for 3D Scanning

Thousands of boxes and fixtures required precise scheduling for 3D scanning. We built a conveyor‑based machine that coordinates with a Nikon X‑ray system, orchestrating the flow of parts to minimize downtime and ensure accurate imaging.

By automating the scheduling and transfer, the system boosts efficiency and seamlessly integrates scanning into the production workflow.

IP application in progress (details confidential)

Precision Pressing for Ultra‑Thin Wires

Connecting ultra‑thin wires to circuit boards manually resulted in inconsistent quality and high rejection rates. We engineered a precision pressing machine that applies controlled pressure, ensuring reliable contacts every time.

The machine improves connection quality and speed while reducing waste and operator fatigue.

Earth–Moon Distance Calculation

Developed a Python algorithm to rapidly calculate the distance between the Earth and the Moon using astronomical data. Leveraging scientific libraries, the program delivers accurate results in a fraction of the time required by manual calculations.

This tool demonstrates how efficient coding can accelerate scientific research while enhancing accuracy.

SolidWorks Assembly Macro

Maintaining thousands of SolidWorks assemblies was time‑consuming and error‑prone. We created a custom macro that automates part updates across multiple files while preserving dependencies and relationships.

The macro reduced weeks of manual work to hours, improving accuracy and freeing engineers to focus on design.

Custom JavaScript Program

For a research project at Penn State University, we developed a bespoke JavaScript application tailored to the client’s specific requirements. The program provides an intuitive interface and robust functionality not available in off‑the‑shelf software.

The solution highlights the versatility of custom code in solving unique problems and enhancing productivity.

Glucose Sensor Algorithm

To improve the accuracy and responsiveness of a medical glucose sensor, we developed an algorithm that processes sensor data, filters noise and delivers precise readings in real time.

This project showcases how algorithm development can enhance medical device performance and patient care.

Voice Cloning

We built a Python‑based deep learning system capable of synthesizing speech that matches a target speaker’s voice. The project involved audio preprocessing, neural network design and extensive training to achieve natural‑sounding results.

This technology enables personalized voice applications, from assistive devices to entertainment.

Browser Extension Design

Developed a lightweight Chrome extension using HTML, CSS and JavaScript to automate repetitive tasks and customize the user experience. Careful UX design and iterative testing ensured a seamless and user‑friendly product.

The extension boosts productivity by simplifying daily workflows.

Stock Analysis Program

Created a Python application that collects financial data, computes technical indicators and visualizes market trends. Users can explore investment strategies and risk metrics through interactive charts.

This tool empowers investors with data‑driven insights and customizable analysis.

Fair & Interpretable Heart‑Disease Prediction

Designed a machine learning pipeline using the UCI Cleveland dataset to predict heart disease, incorporating fairness constraints and interpretable models. Techniques included data cleaning, feature engineering and model explanation.

The project demonstrates how AI can deliver accurate predictions while maintaining ethical standards and transparency.