[connoronthejob]

Neat.

As a mechanical engineer, I feel the part of my job is safe from AI for the time being. I don't think quality training data for good mechanical design exists.

3D CAD is only part of good design. To a tinker-er that is 3D printing simple parts, an STL is fine. But most parts that matter require far more design consideration and detail than simply the geometry data that an STL (or other 3D file) provides.

The majority of parts are accompanied with a drawing, and that is where the real design actually is found: Tolerances, GD&T, materials, processing notes...

Even then, most of the calculations and considerations to build the model and drawing are not explicit in the design documents: Nothing about a drawing of a stainless steel part tells you WHY it must be a stainless steel part. I don't think there is a large set of well documented designs out there to act as training data for an AI system to design an assembly beyond basic 3D parts.

The authors identify this gap, but it's a fundamental problem with the wholesale move to AI in mechanical design.

[pyottdes]

Agreed. At the end of the day, manufactured parts are driven by constraints outside of the CAD environment so analyzing 3D data as the foundation of an AI system strikes me as attacking the problem from the wrong direction. i.e. Simple optimization of a part for injection molding can take it from requiring a bunch of side actions and collapsing cores to a simple 2 sided mold and save hundreds of thousands of dollars in tooling. None of that is obvious from 3D data alone.

That said, I am excited for AI assisted CAD tools. Things like creating and applying global variables to an existing part, complex assembly analysis for part reduction or just making a starting base part can be incredibly tedious and are low hanging fruit for improving CAD workflows with AI imo.

[8note]

a lot of the why is encoded elsewhere in mechanical engineering at least - the tables, the formulas, textbook problems, engineering reports.

one of the challenges to making a good data set might be around bad designs and why they failed. if we get to a mechanical agent, its going to need to understand that brass was a mistake and redesign a part as steel and change the design for the new contraints

unlike code, that kind of train of experiment i think will be a lot more expensive to make, since you might actually want to create those parts along the way and not just pretend

[analog31]

Amusingly, I tried. Your job is safe. ;-) I'm a scientist. I tried using a couple of free 3d modeling tools that are programmable in Python, using Claude within VS Code to design a part according to my prompts. It was a simple plate with some holes for electrical connectors, switches, etc. Of course it let me make some noobie design mistakes, such as making the hole exactly the same diameter as the thing that went into it, plus or minus of course. And I would have had to really scrutinize the Python code to notice that I got one of the diameters wrong altogether.

Just judging from this experience, the effort would rise exponentially with the complexity of the part, not to mention assemblies. The designers earn their keep. I get really bad eyestrain with any task that requires staring at the screen while doing fine mouse work, so I just can't use CAD. On the other hand, I can code all day because I'm not closely focused on the screen when I'm typing.

[NamlchakKhandro]

hold my beer.

the apocolypse needs to happen sooner so we can pop this bubble.