3D Printed Multi-Material Fractal

This project happened a long time ago, but I clearly remember how excited I was about having:

  • A real application for a multi-material AM composition
  • A connection to someone who could print it for me on a very expensive machine

Multiple materials within a single AM print are still today quite under-studied and under-developed. There are a lot of implications in joining two or more materials that have different properties and processing requirements, even when they belong to the same family. I will not get into the technicalities of this here, but it should be noted that parts are not typically optimized for multi-material processing; you don’t just find applications out in the wild.

For this reason, I spent a lot of time during my PhD coming up with placeholder geometries as a benchmark to test and validate my multi-material processes and machines. I created many variations to simulate real-case application scenarios, to challenge my process. But working on fictional geometries is never as fun as working on real cases.

So, you can imagine my excitement when I was approached by someone from the Topology Optimisation department at DTU for guidance on how to print a negative Poisson matrix, a structure that expands sideways when stretched, the opposite of most materials. Fengwen Wang, is a computational designer and researcher, and never managed to test her designs in real life due to limitations in manufacturing techniques. She showed me her designs; we selected one, and we worked through the technicalities to make the file printable.

The part was printed at ETH Zurich, on an Objet Connext 500, with Tango Black and Vero Clear as materials. I was there to extract the parts from the machine, and I still remember the chemical smell and the humming buzz of the sleeping printer. We washed the parts to remove the goo that is characteristic of this process. After they were dry, I carefully pulled the edges of the matrix, and I watched in awe as it expanded in all directions. It worked. What a wonderful exercise, and what a wonderful proof of concept.

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