Student Projects

Additive manufacturing makes it possible to fabricate complex structures and multi-material parts for demanding applications such as aerospace or medical devices. Standard testing and monitoring methods are often insufficient to characterize heterogeneous samples and interfaces, as they are usually limited to linear displacement and force measurements. This is insufficient to understand the mechanisms happening in heterogeneous components with region-dependent mechanical properties. To design better AM parts, we need to see how the material deforms across the entire surface. That’s where Digital Image Correlation (DIC) comes in: a computer-vision technique that provides full-field displacement and strain maps during mechanical tests. Top research labs and industries use DIC to understand how parts bend, stretch, crack, and fail — making it essential for studying the mechanics of advanced materials. In this thesis, you will build your own DIC system from scratch - hardware + software - and test it on mechanical tests in our lab.

Create a high-precision dual-camera DIC setup running on Python capable of measuring strain fields during mono-axial tests. Potentially extend it to bending, shear, and dynamic tests. 1. Explore: quick dive into DIC and the open-source Python libraries openCV and Pyxel 2. Build the hardware setup (camera, lenses etc already waiting for you): • Design, assemble and align a single and a stereo-DIC configuration • Sync the imaging system with the tensile testing machine (trigger + data flow) 3. Make it smart with Python • Implement calibration, acquisition (OpenCV), processing and visualization (Pyxel) routines • Automate as much as you can (your creativity is welcome!) 4. Validate the system (there you get to break things…) • Perform tests, extract strain fields, compare with reference measurements • Demonstrate accuracy and repeatability for 2D full-field strain Final deliverables: A working DIC setup, user guide, and your thesis!

nmartin@ethz.ch

Nanyuan Zhang, PFA E93, nanyuan.zhang@inspire.ch Mert Ilten, PFA E93, mert.ilten@inspire.ch