Working with Algorithm-Generated Design: Panelists Respond to Audience Questions

CIMdata’s Keith Meintjes and Advanced Engineering Solutions’ Andreas Vlahinos as panelists for a LIVE roundtable chat on topology optimization (January 31, 2017, 11AM Pacific / 2PM Eastern).

CIMdata's Keith Meintjes and Advanced Engineering Solutions' Andreas Vlahinos as panelists for a LIVE roundtable chat on topology optimization (January 31, 2017, 11AM Pacific / 2PM Eastern). CIMdata’s Keith Meintjes and Advanced Engineering Solutions’ Andreas Vlahinos respond to questions from the LIVE roundtable chat on topology optimization (January 31, 2017, 11AM Pacific / 2PM Eastern).

Several weeks ago, CIMdata‘s Keith Meintjes and Advanced Engineering Solutions’ Andreas Vlahinos joined me in a roundtable talk on optimization. At the heart of the discussion is the following question: Would you be willing to put your trust in algorithm-generated designs? (The webcast is now archived online here.)

In the past, engineers relied on their industry knowledge, experience, and aesthetic sensibilities to decide the shape of a product. Today, they have the option to turn to generative design, topology optimization, and other technologies to solicit input from AI-like algorithms. The new approach turns the traditional workflow upside down.

As Solidworks CEO Gian Paolo Bassi put it, “The era of design and validate is about to end. We’re entering the era of optimize and manufacture.” (The comments were made during his keynote speech at the recent Solidworks World 2017 conference in LA. For a full report, read “SWW17: From Design and Validate to Optimize and Manufacture.”)

During the webcast, we conducted two live polls with the audience.

Poll #1: If the software’s algorithm suggests a shape that seems counter-intuitive, would you trust it?

  • Yes, I’ll trust it.
  • No, I’ll reject it.
64.1% of the audience responded they would trust the software’s answer. 35.9% said they would reject it.

Poll #2: Do you feel that topology optimization software is too difficult to learn?

  • Yes, it is.
  • No, it isn’t.
21.2% of the audience said it is difficult to use. 78.8% doesn’t think so.

During the Q&A session of the LIVE webcast, Keith and Andreas received more questions than they could address on the spot. Both panelists were kind enough to offer written replies, which are published below.

Audience comment [on Poll #1]: Wish there was a third answer—I would investigate it.

  • Keith’s Answer: There is a third option: Understand why the topology optimization software is giving the result it does.
  • Andreas Answer: You are correct. We always investigate the load cases, the units, the manufacturing constraints, and the mesh quality to understand why we get these results.
Audience Q: Most topology optimization (TopOpt) is done in the structures world. What’s the current status of applying topology optimization in fluids world?

  • Keith’s A: TOSCA in particular can do optimization for fluid flow. But that is on much shakier theoretical ground than structural optimization. It relies much more on heuristics (informed guesses) and brute force computing power.
  • Andreas’ A: In addition to TOSCA Fluid all major CAE vendors, national Labs and universities are working on Multi Physics topology optimization
Audience Q: Are there considerations to be aware of, where optimization algorithms create biased results? How about adding manufacturability constraints into the solution?

  • Keith’s A: The results are always biased. The software is searching for the top of a nearby hill. What they find depends on the starting point and the path taken. Except for the simplest linear problems, there is no single ‘correct’ solution. Many solutions include manufacturing constraints like die direction. But, there are often constraints that cannot be put in the software, so the TopOpt solution needs to be modified. Prof. Shea has an interesting example where they were designing roof trusses for a stadium, but the software suggested structures that intruded on the audience sight lines. So, they had to program additional constraints they had not thought of before.
  • Andreas’ A: The major TopOpt codes have manufacturability constraints such as symmetries, extrusion, casting, stamping, radial filling, minimum/maximum member size, etc. When we run the same problem with different codes we get similar results but not identical.
Audience Q: [What is your] experience using SolidThinking Inspire?

  • Keith’s A: Altair has done a great job of making Inspire a single-user tool that requires minimum expertise in CAD and Finite Element Analysis.
  • Andreas’ A: I think this is the right direction to embed TopOpt within the CAD environment. We should start with the available design space then use the performance requirements in TopOpt and finally we can start the CAD geometry building.
Audience Q: Has the panel come across a situation where an optimal design offered by the software is rejected by higher executives because it is not ‘sell-able,’ especially for mass-market products?

  • Keith’s A: By contrast, these designs are often visually appealing. It is, of course, possible that they may be rejected because they are more expensive to manufacture.
  • Andreas’ A: Several times. Some of the reasons are: the manufacturing cost is high, the time to production is high, the material properties for the specific geometry are not validated.
Audience Q: It was mentioned earlier that consideration must be given to statistical variation in final selection of designs. Do current Topology Optimization software apps provide the utilities to incorporate statistical variation in the modeling?

  • Keith’s A: Autodesk (Project Dreamcatcher) clearly intends to put variation into the analysis. As Andreas said in his introductory remarks, you can always add robust design as a step after TopOpt.
  • Andreas’ A: I have not tried Dreamcatcher but the major TopOpt codes do not include statistical variations. If your performance requirements include quality metrics (i.e. this requirement x needs to have six sigma quality) then you need to perform a DFSS study on your final design.
Audience Q: Can you include “testing” at the CAE input level, OR must you wait for physical testing/failure?

  • Keith’s A: The idea is that TopOpt should give you first-time capable designs that you expect to pass validation testing. It is hard for me to imagine how a design-build-test-break-redesign iterative cycle could be at all effective here.
  • Andreas’ A: I couldn’t agree more with Keith’s answer. In addition, if the TopOpt gives you very different shape that your previous design you should reexamine your loading conditions. In that case, an experiment or an IoT process may be necessary to validate your loading conditions.
Audience Q: What is a good package for someone starting in this field to start with?

  • Keith’s A: If you have the Altair Hyperworks suite, SolidThinking Inspire is a good start and would be “free” to use with your existing license token pool. This capability is being embedded in CAD tools like CATIA, NX, etc. Andreas mentioned that there is open-source and public-domain software available.
  • Andreas’ A: You can find free Applets and Software at . They work great on desktops, tablets and phones.
Audience Q: Would you re-evaluate your resulting CAD in the same software as a first-cut evaluation?

  • Keith’s A: Probably not, and there is a problem. As Andreas mentioned, the path from TopOpt results to CAD is broken. Once the design is realized in CAD, it is sufficiently different that it has to go through a CAE evaluation step. In addition, the optimization may be based on linear analysis, but you have to assess possible nonlinear issues like fatigue and durability.
  • Andreas’ A: Typically we run the topology optimization and the validation steps with different codes (i.e. Genesis. Optistruct or TOSCA for topology and ANSYS, NASTRAN or Abaqus for validation)
Audience Q: [Can] a person with good experience in FEA make a smooth [transition] to Topology optimization?

  • Keith’s A: To do TopOpt effectively requires expertise in CAE, CAD, manufacturing, and product performance requirements. Being an FEA specialist will certainly help if you are part of a team, but the people who do this best seem to simply be acknowledged by their peers as great engineers.
  • Andreas’ A: I don’t see it as a cross over, it is complimentary. Every good design engineer should use topology optimization during the design concept generation.
Audience Q: Is setting up boundary conditions the same in Topology Optimization as they are in traditional FEA?

  • Keith’s A: Yes. But, there are additional constraints such as allowable (physical) design space and attachment points to other parts which are not explicitly stated in the conventional design process.
  • Andreas’ A: Yes Typically is a combination of load cases with the addition of manufacturability constraints.
Audience Q: How do you think artificial intelligence will affect design optimization in the near future?

  • Keith’s A: Yes. Take a look at what Jeff Kowalski, CTO of Autodesk, has to say on the topic.
Audience Q: Do you see Topology Optimization more for the R&D team than the design team?

  • Keith’s A: Not really. It’s more an issue of getting the generative design activity upfront, ahead of where we today do detailed CAD. In many organizations, they do not have anyone assigned to do that job.
  • Andreas’ A: Absolutely not. The hardest part is to have the design requirements clearly defined up front. If the requirements are defined the Topology optimization process is fun.
Audience comment to Andreas: Gordon Willis (former Chief engineer at Ford in the 1990s) tried to develop and AI-based system for automotive development (called his company Vulcanworks) but he was WAY ahead of his time (he did this around 2001-2003) and gave up.

  • Andreas’ A: I am very familiar with Gordon. We worked together on an “Advanced Engineering Environments” project funded by DOE. In addition to good ideas and tools you need an internal champion in the management of a large corporation to recognize the vision, adopt it, adapt to achieve it.

To listen to the original webcast, go to /de/optimize.

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Kenneth Wong

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