# Topology Convergence problem

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Rahul,

thanks for the answer. I have increased the no of iterations to 150. Now it converged, but the result is still very bad.

The displacement is -1.5e4 in the convergence graph. I dont know whats wrong...

Could you please check my model?  I attached it in my first post.

wan

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Hi @wan,

Looks like You have only an upper bound but no lower bound. Can you create an upper and lower bound for your displacement constraints?

If you have only one bound, solver will only solve for that constraints. +Ve bound in your case.

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Hi,

thank you for the answer. I tried what you suggested. Unfortunately, it do not work. It converged, but the displacement is to high..

lower bound was: 1,5

upper bound was: 2

Could you please take a look at my model?

Thank you very much

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Can you have a lower bound like -2 or -3 units?

What you have is in positive direction. Give a constraints in negative direction component.

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I dont understand why it the displacement is negativ.

My Model is like a simple beam in bending. I have a force in positive

direction. So i think the displacement is always positive, too.

Is that right?

thank you

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Can you load the model in HyperView and post process the results?

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This is the result in Hyperview. As you can see the force is positive. But why is the displacement during the optimization in negative direction?

If i run only the Analysis  i get positive displacment. Whats wrong?

Thank you

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Hi @wan

Are you constraining the model some where in the middle?

If yes, then the results seems to be OK. If you observe the results, the nodes which are going into negative displacement are on the other side of the constraint.

So, due to moment of force, the nodes are tend to move in other direction of force (Something like a See-Saw)

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Thank you for your reply. Yes ist like a See-Saw, but the negative displacement is very low in relation to the positive disp.

Anyway I have implement your proposal. The displacement constraint is now -2mm ... +2mm

But the result is the same: OPTIMIZATION HAS CONVERGED but INFEASIBLE DESIGN (AT LEAST ONE CONSTRAINT VIOLATED)

The optimization takes 845 iterations!

Could you take a look at my model please? I attached the .fem and the .out in a zip file

(please dont be confused, i named the constraint dispx, but it means y direction.. only the Name is wrong.)

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My suggestion: Have two displacement responses. One for those nodes where you want to constrain the positive displacement

The other response for those nodes (on the other side of the model constraint) to constraint negative displacement.

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Thank you for the suggestion. I have implement it but sadly with no success. The result is similar.

I think the problem has a different reason. I think it has something to do with the Minimum member size (mindim). Until now i have done every optimization with mindim 15.

Now i tried it without mindim and it works! The displacement curve looks good.

.

But now i would like to know why the mindim do not work? The average element size is 4mm.  I tried mindim 15mm, 18mm, 20mm, and 25 mm.

Always i see those huge peaks in the displacement curve.

Can you help me again... Thank you

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Hi Wan,

Somewhere, the minimum dimension constraint is restricting the objective/design constraint to proceed further. This needs to be checked.

I shall check with your previously shared model and will get back to you soon.

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Hi Prakash,

have you already found out something?

Thanks a lot

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Hi Wan,

So, what ever I change, the result is same which is very strange.

I am checking with one of our experts. I will update to you soon.

Meanwhile can you let me know what is the version and updates you have for HyperWorks?

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Hi Wan,

Can you share your updated model with me?

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Hi Prakash,

Nothing changed. I tried many variations but i get always the same bad result.

Can you estimate how long it will take to check this model with one of your experts?

Thank you

wan

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Hi Prakash,

i still have that problem with that model. Have you any solution for that?

Thank you!

wan

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Hi Wan,

Usually design without any manufacturing constraints seems to be best design but not manufacturable. This is why we add manufacturing constraints.

These manufacturing constraints may not go well with the design constraints and this is why the optimization fails.

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