3D Shape Segmentation With Projective Convolutional Networks

This is an interesting summary of an approach for shape segmentation. I think it's pretty cool how often VGG-16 gets used for transfer learning with good results. It's amazing that these models can represent enough knowledge to generate 3-D surfaces from single images. (I also like how many folks use airplanes as examples : - )



There's a website for the ShapeNet data set that they used as a benchmark in the video, and this paper describes the initial methods folks developed during the challenge right after the data set was released. That's a pretty neat approach. It reminds me a bit of the AIAA drag prediction workshops.

Fun with Machines that Bend

I really like his 3-D printed titanium part at about the 8 minute mark, and the chainsaw clutch at minute 10 is pretty neat too.


The eight "P's" of compliant mechanisms:

1. Part count: reduced parts count with bending parts instead of hinges and springs
2. Production processes: lower price through processes like injection molding
3. Price: lower because of reduced parts count and affordable processes with reduced assembly
4. Precise motion: no backlash (yea!),
5. Performance: no need for lubricants, reduced wear
6. Proportions: can be made at small scale with photolithography
7. Portable: lightweight
8. Predictable: the operation of the mechanism can be well-known and reliable

Engineering Sketch Pad

I haven't heard of Engineering Sketch Pad (source code as part of OpenMDAO, and here) before, but this is yet another NASA sponsored open source tool that could be useful to you for aircraft conceptual design. I read about it in a post on Another Fine Mesh about some interesting research the folks at Pointwise are doing. It reminds me of, but is different from, Open Vehicle Sketch Pad.

There's a seminar on the software given by one of the developers up on a NASA site: The Engineering Sketch Pad (ESP): Supporting Design Through Analysis. (yea, DARPA!)

It has some neat features that make it useful to support high-fidelity analysis. It creates watertight geometry, it can carry attributes with the geometry that could guide mesh resolution, it does "conservative" data transfer for discipline coupling (match a solver's numerical scheme), and most of its parts are differentiable which is useful for optimization.

I added this to my list of Open Source Aeronautical Engineering Tools.

OpenLSTO plus InverseCSG

I was recently excited to learn about the OpenLSTO and InverseCSG projects, and that got me thinking: can we automate topology optimization interpretation for a 3D part with open source tools?

Topology optimization results are usually a discrete set of density voxels (as from ToPy) or a triangulated mesh (as from OpenLSTO). There is an interpretation step often required to take this result and turn it into something that you can fabricate or incorporate into further design activities. In the case of OpenLSTO you are getting what your manufacturing chain needs (an stl file) if you are 3D printing.

Interpreting the results of a topology optimization can be a time consuming manual process for a designer. While the steps to interpret a 2D topology optimization result can already be automated with a complete open source tool-chain, 3D is harder. I demonstrated in this post how the 2D bitmap output of ToPy can be traced to generate dxf files that you can import and manipulate in a CAD program. On the other hand, here’s an example I did that demonstrates the more manual process for a 3D part.

InverseCSG recovers CAD from model

The MIT Computational Fabrication Group has a page up with the abstract and links to the paper and video. The InverseCSG folks took a program synthesis approach to enable them to generate CAD boolean operation "programs" from the 3D model "specification."

OpenLSTO: New Open Source Topology Optimization Code

Optimized 3D Cantilever from OpenLSTO Tutorial

I was excited to see this short mention of a new open source topology optimization code in the Aerospace America Year in Review.

In July, University of California, San Diego published open-source level set topology optimization software. This new software routinely runs 10 million element models by adapting and tailoring the level set method, making design for additive manufacturing immediately accessible.
New computing tools, international collaboration spell design progress

The software site for UC San Diego's Multiscale, Multiphysics optimization lab has the basic license information, and links to documentation and downloads. The source code is up on github as well.