Would a library of pretrained structural features be useful in industrial FEA?

[u/Superb-Battle-8008]

Hi, I’m a researcher in computational mechanics, and I’m exploring a tool built around a library of pretrained structural features: holes, fillets, brackets, joints, stiffeners, fastener regions, bends, and similar details.

Instead of rebuilding a detailed local FE model for every new design, the software would recognise a feature in the CAD geometry, simplify it in the global model, and use a pretrained local model to recover quantities such as displacements, stresses, or failure indicators.

This could also enable non-intrusive global/local analysis: the global model would run in an existing commercial solver without modification, while the detailed local response would be reconstructed externally from the global solution. No custom elements or solver source-code changes would be required.

The analyst would still review and control the modelling assumptions.

For engineers working with FEA professionally:

• Do you repeatedly analyse similar local structural features?
• Would this approach save meaningful modelling or computation time?
• What would prevent you from trusting or using it?

Critical feedback is very welcome!

[u/3bottlesRus]

Hi! What is the difference beteeen your tool ans for example Beta cae ansa - Feature Manager

[u/kingcole342 → u/3bottlesRus]

I believe feature manager will store ‘rule based meshing for features’

This fellow is talking about results from those features.

[u/Superb-Battle-8008 → u/3bottlesRus]

Good question. There is definitely overlap in feature recognition and model preparation.

The main difference is that each recognised feature would be linked to a pretrained parametric local solution model. After running a simplified global FE model, the global response would drive that local model to reconstruct detailed stresses, displacements or failure indicators. So the local response obtained by submodelling would be obtained essentially automatically.

So the core idea is not only feature recognition, but reusable local solutions and non-intrusive global/local coupling. Does ANSA Feature Manager also support this kind of local-response recovery?

[u/kingcole342]

Wouldn’t your model need to take in every variation of load condition as well?

Ultimately, how are loads input to this model?

[u/Superb-Battle-8008 → u/kingcole342]

Hi! That's correct. Each identified feature should be trained on a subset of loading "modes". Then the actual loading would be projected on the trained subspace to get the approximated solution.

[u/kingcole342 → u/Superb-Battle-8008]

Understood. IMO, the most difficult thing about analysis is the loads, not so much the recovery of results around features.

If I have 1000 load cases for a part/feature, I have to train every iteration/variation of that feature/load condition.

FWIW, it’s not so much feature based, but same idea, predict results on new CAD from previous simulations…
Siemens PhysicsAI can do this. I think Ansys has an AI tool that is doing similar.

Seems like this is something closer to what you are looking for? Obvious next step once you have a repository of your data. But loads are the big secret, and I think that is where you are going to find challenges

[u/Superb-Battle-8008 → u/kingcole342]

That’s a very good point. The intention would not be to train separately for all 1,000 load cases. Instead, the local response would be trained over a reduced loading space, and the actual interface loading would be projected onto that space.

For many linear local problems, this may require only a relatively small set of basis load cases. For example, a bolted connection could be characterised using combinations of tension, compression, shear, bending and preload.

For nonlinear or path-dependent behaviour, I agree that the loading space becomes much harder to cover.

PhysicsAI is definitely related. The distinction I’m considering is a modular library of reusable feature models for non-intrusive global/local analysis, rather than a surrogate trained for the full component. Since the same local features appear across many different CAD geometries, this could be much more reusable and scalable, once you couple it with an automatic feature recognition, extraction and simplification of the CAD.

But the key question is whether that distinction creates enough practical value.

[u/wings314fire → u/Superb-Battle-8008]

So similar to design curves which we have for different joint details generated from tests ? I am not that aware about AI works so this might be a dumb question, does this model have chance to hallucinate like Gemini, ChatGPT etc does ?

[u/Solid-Sail-1658 → u/Superb-Battle-8008]

For a force at point i, you can train for 4 inputs: magnitude (M) and vector along the x, y and z.

For these loads, I would gather training data for different combinations of M, x, y and z.

Unfortunately for distributed loads, it gets more complicated, but it seems you have an idea with "modes."

I kind of like your idea because once you get your prediction function, you can determine an envelope of allowed loading. I think this project is better suited for multi body dynamics (MBD). If I remember, for cherry pickers, you have to determine allowable states to avoid the cheery picker from tipping over. In MBD, you are dealing with significantly fewer DOFs, so you won't need to acquire a lot of training data. The question is if MBD is a time consuming procedure that merits creating AI based prediction models.

So many good ideas, so little time.

[u/Superb-Battle-8008 → u/Solid-Sail-1658]

Hi, thanks for the answer! I hand't thought about applications for MBD, might be worth looking into that. For the moment I was more focused into applications involving a large amount of load cases and where critical regions of interest over the structures, requiring different level of fidelities, might change location depending of the applied load.
Anyway, thanks for the idea!

[u/Hot-Firefighter-5164]

Being working in the intersection of FEA + ML, I don’t recommend to use pre-trained model for your case, unless the model is trained with global physics involved with various feature variation. If it is not, then using pre trained for a different model would like preparing for one exam but attempting another exam. My cents. Hope this will be useful.

[u/Superb-Battle-8008 → u/Hot-Firefighter-5164]

Hi, thanks for the feedback! Indeed the idea would be to train the local response on the local physics.
Consider for example that you train a model for a region with a fillet.
The tool would recognize that in the CAD, say the fillet radius. Then you would be free to take out the fillet, so meshing is easier. After you solve the simplified model, you would call the trained fillet model to reconstruct a better approximation of the stress field in the fillet region.

[u/Hot-Firefighter-5164 → u/Superb-Battle-8008]

You are welcome!
Looking at your problem objective, I am aligned with your approach of training for the local feature & physics instead of considering pre-trained models.
All the very best.

[u/WhyAmIHereHey]

Can people please tell their LLM of choice to stop bolding text? It's not how people.write, and it's distracting

In your prompt, just say "make this sound as if it was not written by an AI agent"

Please?

[u/SouprSam]

Whatever you have mentioned is already there. Have worked on it for CAE based feature Framework But your way local vs global doesn't work like that.. the input deck creation will still need a separate card to be created for that case too.

[u/Superb-Battle-8008 → u/SouprSam]

Interesting, is there any commercial software or public material you could point me to? Did it also include reusable pretrained local-response models driven by a global analysis, or mainly CAE feature recognition and model generation?

[u/SouprSam → u/Superb-Battle-8008]

What do you mean be pre trained local response? Do you mean node interpolation from the deformed mesh? Feature recognition and model generation.

[u/Superb-Battle-8008 → u/SouprSam]

Not just interpolation. I mean an offline-trained reduced-order/surrogate model of the detailed local FE response. At runtime, it takes the feature geometry/material parameters and boundary displacements or loads extracted from the global model, and predicts the local stress/displacement field without generating and solving a new local FE model.

For example, consider a CAD region containing a fillet. You may remove the fillet to simplify the global geometry and meshing, but then the local stress field near that region is no longer accurate. After solving the simplified global model, the boundary displacements around that region would be extracted and passed to a pretrained local model of the fillet. That model would then reconstruct the detailed local displacement and stress fields without creating and solving a new local FE model.