How Bucharest Engineering Students Used Onshape to Build, Publish, and Win

Quick Summary

• 11 Politehnica University students used Onshape during their summer practice at PTC to build real engineering projects.
• Projects ranged from digital twin robotic arms to AI-enhanced CNC machines, several of which were published in international journals.
• Cloud-native CAD gave students the speed and tools to go from idea to prototype without friction.

At Politehnica University of Bucharest, engineering education doesn’t stop at the classroom door. Each summer, students are required to complete a practice stage – a hands-on project developed in partnership with a real tech company.

This year, 11 students worked under my coordination at PTC’s Bucharest office, which sits right next to campus, using Onshape to design, collaborate, and bring their ideas to life.

What began three years ago with just three students has grown into a full STEAM learning program. The projects that emerged this summer ranged from AI-enhanced CNC machines to 6-axis robotic arms with digital twin capabilities – each one tackling a genuine engineering challenge, and each one built with Onshape at the center of the workflow.

Here’s a look at what they built.

Project 1: A Digital Twin System for Industrial Robotic Arms

Onshape features used: API, Custom Features, Fast Iteration

This project set out to make digital twin technology accessible. Students designed a low-cost system that links a physical robotic arm prototype to its virtual counterpart in Onshape, enabling real-time, bidirectional monitoring and control. Built on a Raspberry Pi platform with web and API connectivity, the system achieved approximately 200 ms synchronization and ±0.3° accuracy. Solid performance for a student-built prototype.

The framework supports trajectory testing, performance evaluation, and early fault detection, with clear potential for applications in automated sorting, education, and rapid prototyping.

The project was featured in “Digital Twin-Based Control and Monitoring of Industrial Robotic Arms Using Cloud CAD Platforms” in the International Journal of Modeling and Optimization.

Project 2: Automating Brass Insert Placement in 3D-Printed Parts

Onshape features used: API, Simulation, Collaboration, Custom Features

This project is an automated system for embedding brass threaded inserts into 3D‑printed parts using the Onshape API to directly read insert coordinates and orientations through mate connectors for precise design‑to‑machine execution.

The 3D model integrates Python‑based motion control with a compact XY‑Z mechanism, automated insert feeding, and sensor‑based depth monitoring to achieve highly repeatable insert placement. By replacing manual alignment and heating with a fully automated workflow, the system improves accuracy and efficiency by over 70% and is well‑suited for scalable post‑processing in additive manufacturing.

The project was featured in “Integration of CAD-Guided Automated Insert Placement for Enhanced 3D Printing” in the journal Proceedings in Manufacturing Systems.

Project 3: A Soft Robotic Gripper Inspired by Caterpillar Locomotion

Onshape features used: Simulation, Fast Iteration

This project explored the frontier of soft robotics. Students designed a pneumatically actuated gripper using multimaterial 3D printing, combining flexible TPU fingers with rigid PLA structural elements. Internal pneumatic channels enable controlled bending, allowing the gripper to handle delicate objects without damaging them.

Initial testing validated single-finger performance and identified sealing improvements needed for full multi-finger operation, exactly the kind of iterative discovery that makes a strong research contribution.

The project was featured in “Pneumatically Actuated Soft Robotic Gripper Using Multimaterial 3D Printing” in the journal Proceedings in Manufacturing Systems.

Project 4: An Autonomous Pallet-Handling Robot for Factory Environments

Onshape features used: Collaboration, Fast Iteration

Logistics is one of manufacturing’s most labor-intensive domains. This project tackled it head-on with a fully modeled autonomous robot designed in Onshape, capable of transferring pallets between conveyors and loading or unloading them from trucks — all without human intervention.

The design process itself was a lesson in collaborative engineering. Multiple team members worked in parallel within Onshape, iterating quickly from concept to final model without the friction of file transfers or version conflicts.

Project 5: An AI-Enhanced CNC Laser Cutter

Onshape features used: Easy-to-Learn CAD, Fast Iteration

This project combined traditional fabrication with computer vision. Students designed an AI-enhanced CNC laser cutter in Onshape that can identify remaining material areas on a sheet, recommend optimal placement for upcoming cuts, and detect potential issues before they become errors.

The result is a smarter, less wasteful cutting workflow — and a demonstration of how AI and CAD can be integrated from the design stage rather than bolted on afterward.

What These Projects Have in Common

These projects aren’t theoretical exercises. They are the product of students engaging with real engineering problems under real constraints, with real deadlines, and with findings significant enough to publish and present at international conferences.

What made the difference? In every case, students pointed to the same things: the ability to iterate quickly, collaborate without friction, and access powerful tools, including the Onshape API and simulation, without a steep learning curve or hardware requirements.

Cloud-native CAD doesn’t just change how students design. It changes what they’re able to accomplish.

About the Author

Alexandru Cazacu is PTC’s principal education solution engineer and lecturer based in Bucharest, Romania. He coordinates the summer practice program at PTC’s Bucharest office, where engineering students from Politehnica University work on real-world design and manufacturing projects using Onshape.