Academic Lab Module & TA Experience
OnShape Digital Twin
I developed a lab for Dr.Zhang at Purdue University. The lab dealt with teaching students how to use OnShape's API to create and use a digital twin of a small custom robot arm controller. The setup used Arduino and Potentiometers and mapped their values into the OnShape model's joints to create a real time digital twin. The goal was to create a digital twin that is online, platform agnostic and can be recreated with off the shelf components
OnShape
Google Collab
REST API
Reverse Kinematics
Arduino
Jupyter Lab
OnShape Assembly
This is the OnShape Assembly of the Digital Twin
Cloud Native Architecture
Developed a real-time digital twin of a robotic arm using the Onshape REST API, structured as a hardware-to-cloud pipeline where Arduino sensor input drives parametric CAD assembly motion. Designed the architecture to be platform-agnostic and scalable across student machines by separating hardware execution from cloud-based collaboration. Built a hybrid runtime model where Google Colab handled shared development and API orchestration, while a local Jupyter runtime enabled direct serial communication with hardware.
Cloud Native Architecture
Developed a real-time digital twin of a robotic arm using the Onshape REST API, structured as a hardware-to-cloud pipeline where Arduino sensor input drives parametric CAD assembly motion. Designed the architecture to be platform-agnostic and scalable across student machines by separating hardware execution from cloud-based collaboration. Built a hybrid runtime model where Google Colab handled shared development and API orchestration, while a local Jupyter runtime enabled direct serial communication with hardware.
Cloud Native Architecture
Developed a real-time digital twin of a robotic arm using the Onshape REST API, structured as a hardware-to-cloud pipeline where Arduino sensor input drives parametric CAD assembly motion. Designed the architecture to be platform-agnostic and scalable across student machines by separating hardware execution from cloud-based collaboration. Built a hybrid runtime model where Google Colab handled shared development and API orchestration, while a local Jupyter runtime enabled direct serial communication with hardware.
Cloud Native Architecture
Developed a real-time digital twin of a robotic arm using the Onshape REST API, structured as a hardware-to-cloud pipeline where Arduino sensor input drives parametric CAD assembly motion. Designed the architecture to be platform-agnostic and scalable across student machines by separating hardware execution from cloud-based collaboration. Built a hybrid runtime model where Google Colab handled shared development and API orchestration, while a local Jupyter runtime enabled direct serial communication with hardware.
Parametric Modeling & Configuration Logic
Engineered the development workflow to comply with Purdue iTaP policies and available software licensing constraints. Selected Google Colab to eliminate local installation barriers and simplify version control and collaboration, ensuring the project remained accessible across operating systems without requiring elevated permissions. Implemented a local Jupyter Notebook backend to bridge Arduino serial communication, enabling real-time hardware integration while preserving cloud-based accessibility. This approach ensured the system remained license-compliant, portable, and reproducible.
Parametric Modeling & Configuration Logic
Engineered the development workflow to comply with Purdue iTaP policies and available software licensing constraints. Selected Google Colab to eliminate local installation barriers and simplify version control and collaboration, ensuring the project remained accessible across operating systems without requiring elevated permissions. Implemented a local Jupyter Notebook backend to bridge Arduino serial communication, enabling real-time hardware integration while preserving cloud-based accessibility. This approach ensured the system remained license-compliant, portable, and reproducible.
Parametric Modeling & Configuration Logic
Engineered the development workflow to comply with Purdue iTaP policies and available software licensing constraints. Selected Google Colab to eliminate local installation barriers and simplify version control and collaboration, ensuring the project remained accessible across operating systems without requiring elevated permissions. Implemented a local Jupyter Notebook backend to bridge Arduino serial communication, enabling real-time hardware integration while preserving cloud-based accessibility. This approach ensured the system remained license-compliant, portable, and reproducible.
Parametric Modeling & Configuration Logic
Engineered the development workflow to comply with Purdue iTaP policies and available software licensing constraints. Selected Google Colab to eliminate local installation barriers and simplify version control and collaboration, ensuring the project remained accessible across operating systems without requiring elevated permissions. Implemented a local Jupyter Notebook backend to bridge Arduino serial communication, enabling real-time hardware integration while preserving cloud-based accessibility. This approach ensured the system remained license-compliant, portable, and reproducible.
Controller
Designed the Onshape assembly using controlled revolute mates and constrained motion limits to reflect realistic robotic kinematics. Structured assemblies and mate definitions to support programmatic manipulation through API calls, enabling hardware-driven animation of CAD components. Organized configuration and mate structures to maintain clarity, scalability, and maintainability as additional joints or sensors are introduced. The Potentiometers used in the controller, the final model uses 3d printed parts that friction fits onto the grooves of the potentiometer
Controller
Designed the Onshape assembly using controlled revolute mates and constrained motion limits to reflect realistic robotic kinematics. Structured assemblies and mate definitions to support programmatic manipulation through API calls, enabling hardware-driven animation of CAD components. Organized configuration and mate structures to maintain clarity, scalability, and maintainability as additional joints or sensors are introduced. The Potentiometers used in the controller, the final model uses 3d printed parts that friction fits onto the grooves of the potentiometer
Controller
Designed the Onshape assembly using controlled revolute mates and constrained motion limits to reflect realistic robotic kinematics. Structured assemblies and mate definitions to support programmatic manipulation through API calls, enabling hardware-driven animation of CAD components. Organized configuration and mate structures to maintain clarity, scalability, and maintainability as additional joints or sensors are introduced. The Potentiometers used in the controller, the final model uses 3d printed parts that friction fits onto the grooves of the potentiometer
Controller
Designed the Onshape assembly using controlled revolute mates and constrained motion limits to reflect realistic robotic kinematics. Structured assemblies and mate definitions to support programmatic manipulation through API calls, enabling hardware-driven animation of CAD components. Organized configuration and mate structures to maintain clarity, scalability, and maintainability as additional joints or sensors are introduced. The Potentiometers used in the controller, the final model uses 3d printed parts that friction fits onto the grooves of the potentiometer
Communication
Established a real-time data flow where Arduino potentiometer inputs stream over USB, are processed and scaled in Python, and mapped into Onshape mate limits via REST API calls. Implemented GET and POST API interactions to retrieve assembly states and update mate values dynamically. Structured the pipeline to handle continuous sensor updates, mapping raw analog values into constrained rotational limits for accurate joint control within the CAD model.
Communication
Established a real-time data flow where Arduino potentiometer inputs stream over USB, are processed and scaled in Python, and mapped into Onshape mate limits via REST API calls. Implemented GET and POST API interactions to retrieve assembly states and update mate values dynamically. Structured the pipeline to handle continuous sensor updates, mapping raw analog values into constrained rotational limits for accurate joint control within the CAD model.
Communication
Established a real-time data flow where Arduino potentiometer inputs stream over USB, are processed and scaled in Python, and mapped into Onshape mate limits via REST API calls. Implemented GET and POST API interactions to retrieve assembly states and update mate values dynamically. Structured the pipeline to handle continuous sensor updates, mapping raw analog values into constrained rotational limits for accurate joint control within the CAD model.
Communication
Established a real-time data flow where Arduino potentiometer inputs stream over USB, are processed and scaled in Python, and mapped into Onshape mate limits via REST API calls. Implemented GET and POST API interactions to retrieve assembly states and update mate values dynamically. Structured the pipeline to handle continuous sensor updates, mapping raw analog values into constrained rotational limits for accurate joint control within the CAD model.