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Lesson content: Abaqus Model
Optimization Tasks Design Respons
Objective Functions
Constraints Geometric Restrictions
Stop Conditions
Postprocessing Workshop 2a: Topology Optimization of a Cantilever Beam With Stamping Geometric Restrictions Workshop 2b: Topology Optimization of a Cantilever Beam With Demold Control Using the Central Plane Technique
Workshop 2c: Topology Optimization of a Cantilever Beam With Symmetry Geometric Restrictions Lesson 3: ATOM Workflow and Options 2.5 hours
L3.2
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亚巨Abaqus Model
The Abaqus model must be ready prior to the tup
哈尔滨博物馆of the optimization
Although not necessary, it is helpful to create ts
that can be ud later to define the optimization
regions
Shown on the right: A t was created to define
the region (cell) where the stamping geometric
restriction will be applied
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An optimization task identifies the type of optimization and the design domain for the optimization.
The task rves to configure the optimization algorithm to be ud
Create an optimization task from the Model Tree or the Optimization toolbox as shown
Choo the type of optimization task accordingly
Each task also contains the design respons, objective functions, constraints, geometric restrictions and stop conditions
In this lecture we discuss the tup of the task for topology optimization L3.4
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Optimization Tasks (2/6)
英语故事小短文For a topology optimization task, the optimization region is lected next
The elements in the optimization region will constitute the design domain
The whole model is lected by default
Often, the optimization region will only be a subt of the model.
For example, on the right we have removed the deformable shaft from the display so that only
the gear is lected as the optimization region
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Having chon the optimization type and region, it is now possible to configure the optimization
The Basic tab of the optimization task editor allows the ur to choo if the load and boundary regions are to be kept frozen
Frozen areas are discusd further later in the context of geometric restrictions L3.6
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Optimization Tasks (4/6)
鲤鱼汤The Density tab allows the ur to change the density update strategy and configure other related parameters
The ttings are only available for the nsitivity-bad method
Tip: The parameters rarely need to be changed; if necessary, u a more conrvative strategy for a more stable optimization
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The Advanced tab allows the ur to switch to the condition-bad approach if desired
The condition-bad approach is usually preferred for stiffness optimization
Note: the nsitivity-bad approach is also able to optimize on stiffness
For the condition-bad approach, the ur can configure the speed of the update scheme and the volume deleted in the first cycle男人更年期症状
The advanced option “Delete soft elements in region” is recommended when solving problems where soft elements may distort excessively and cau convergence difficulty L3.8
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Optimization Tasks (6/6)
For nsitivity-bad optimization the ur may choo between the SIMP and the RAMP material interpolation techniques
RAMP is preferred for problems that are more dynamic in nature becau the interpolation scheme is always concave.
Criteria for convergence can be t here. Default criteria are usually sufficient.
Note: the default penalty factor has been chon carefully.
Values less than 3 shouldn’t be ud.
Values greater than 3 significantly increa the chance of getting trapped in a local minima
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Design Respons (1/3)
Design respons are output variables that can be ud to describe objective functions and constrai
nts
All available design respons for nsitivity-bad optimization are shown on the right
Condition-bad optimization can only have strain energy as the objective and volume as the constraint
Design respons can be a summation of values in the region or maximum/minimum of that region
Design respons can also be summed across steps/load cas L3.10 w w w .3d s .c o m | © D a s s a u l t S y s t èm e s
Design Respons (2/3)
A design respon can be a combination of previously defined design respons
For example, on the right we have constructed design respon D-Respon-3 as a
weighted combination of D-Respon-1 and D-Respon-2
Sensitivity-bad optimization supports the following operators:
Weighted combination
Difference
Absolute difference
