T Topology Optimization: A Long Way To Go · Engineering group manager for simulation at GM...

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TTopology Optimization:

A Long Way To Go

Topology Optimization Round TableWashington, DC20 February, 2018

Keith Meintjes, Ph.D., [email protected]+1.248.891.6434

mailto:[email protected]


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Dr. Keith Meintjes, Simulation & Analysis Leader of the Simulation & Analysis Council and consulting at

CPDA & CIMdata since 2008 (CPDA acquired by CIMdata in 2011) 29 years at GM included numerous positions of technical

and strategic leadership Responsible for GM’s global strategy for high-performance computing Champion of GM’s CAE “Grand Challenge” initiative to improve simulation

capability for all aspects of vehicle development Engineering group manager for simulation at GM Powertrain Part of a team to apply simulation tools for powertrain product development Developed CFD tools for engine combustion Ph.D. thesis (Princeton) on CFD fluid flow simulations with combustion Born in South Africa, holds B.Sc. and M.Sc. from U. of the Witwatersrand

Dr. Keith Meintjes, Executive Consultant Professional background


AbstractTopology Optimization: A Long Way To Go

Topology Optimization (TopOpt) was "invented" in the late 1980s, but the technology was not widely adopted: The software was too difficult to use, and it generated designs that were not manufacturable by conventional means. Now, we have the maturing of additive manufacturing, which has caused renewed interest (or even excitement) in TopOpt. But, in a sense, we have gone from parts we can design but not manufacture, to parts we can manufacture but not design.

Dr. Keith Meintjes was customer #001 for Altair Optistruct, the first commercial TopOpttool, back in the early 1990s. He will point out that, nearly 30 years later, there is a lot of work yet to be done. The path from idea to product is far from even. While many share a vision that Generative Design and Topology Optimization will revolutionize product development, the realities of data flow through the product development process make this seem like a mirage.

Keith will stress that while people are earnestly working within their silos, there needs to be more attention on the end-to-end process of making useful products, communication between the silos.


A Short History Shape and Topology Optimization

Shape optimization Developed in the late 1980s Fairly successful for some applications like stiffening ribs for sheet metal

stampings, lightening holes in aircraft structures

Topology optimization Developed in the early 1990s Some success with almost-2D parts: Struts, brackets, suspension components Low adoption

Codes were very difficult to use Creating useful CAD geometry from TopOpt results was is very difficult 3D TopOpt parts are usually not manufacturable by traditional (subtractive) means

So, why are we here, in DC, today?


Why The Enthusiasm and Excitement? (1)How is it different this time, than it was 25 years ago?

The rise of Additive Manufacturing (3D Printing)

Yes, but also:

New materials

Advances in computational geometry

The incredible rise in computing power Computing is now infinitely fast and vanishingly cheap (by

comparison) We can compute in one second what would have taken the

Apollo moon program engineers 1400 years!


Why The Enthusiasm and Excitement? (2)How is it different this time, than it was 25 years ago?

Advances in IT Digital product definition Cloud Machine learning Big data and analytics

Statistical Optimization Stochastics, robust design

And, the synergies between all of these

Many of us believe this will cause a revolution in product development and manufacturing

Generative Design

Industry 4.0

1

2

34

AI and Machine Learning


“Generative Design” is a collection of tools and techniques that help to create or modify feasible product designs, including geometry, from statements of requirements and constraints.

This is different than the usual paradigm, where CAD (product geometry) is created and then evaluated against product requirements.

GD tools include Topology optimization Shape optimization Rules-driven Parametric CAD Cost and manufacturing optimization …

Generative Design (GD)


Is the use of algorithmic methods to transform requirements into product geometry and design

Often includes optimization within constraints

Is not necessarily based on physics (nor on natural organic processes)

Has long been an idea for architectural design

Generative DesignCIMdata’s Definition


Generative Design

“Integrated performance-driven generative design systems are aimed at creating new design processes

that produce spatially novel yet efficient and buildable designs through exploitation of current

computing and manufacturing capabilities.”

Prof. Dr. Kristina Shea (ETH Zurich)

K. Shea et al, “Towards integrated performance-driven generative design tools”, Automation in Construction 14 (2005) 253–264.


Traditional Component Design ProcessCharacterized by many evaluation iterations

Previous Designs(History)

Design Constraints

Performance Requirements

Design Space

(Context)

Create Design (CAD)

Evaluate Design (CAE)

Issues

Valid Design

Design Iterations

Build


Generative Design ProcessOptimization should directly create feasible and buildable designs

Previous Designs(History)

Design Constraints

Performance Requirements

Design Space

(Context)

Topology Optimiza-

tionvia CAE

Valid Design

Document Design (CAD)

Build


Autodesk Generative Design (Dreamcatcher)

• Such lattice structures can be designed for a specified center of gravity and moments of inertia

• They can be efficient heat exchangers and catalyst beds

• They can also be designed as “pseudo-materials”, with properties like a negative Poisson’s Ratio or zero thermal Coefficient of Expansion

• The innovation options are endless

http://damassets.autodesk.net/content/dam/autodesk/www/solutions/generative-design/lattice-and-surface-optimization-thumb.jpg

http://damassets.autodesk.net/content/dam/autodesk/www/solutions/generative-design/lattice-and-surface-optimization-thumb.jpg


15

Feasible Lattice Printing

Tailored Lattice Size


Topology Optimization Must Generate “Useful CAD”This is a difficult and manual process – unacceptable in a practical workflow

Adapted from: US Federal Highway Administration:http://ops.fhwa.dot.gov/publications/seitsguide/“Systems Engineering for Intelligent Transportation Systems”

Implementation

Image courtesy of Dr. Andreas Vlahinos, Advanced Engineering Solutions:

“Here, the process is broken”


I have had some negative and critical things to say

Most of the software providers get it, and they are working to fix the issues

There are a lot of bright and innovative people engaged

The issues will be addressed, over time

But

There is a lot of work to be done

Neither Topology Optimization nor Additive Manufacturing are silver bullets

It’s not just software: User organizations have their issues

Keith’s Disclaimer


Software is too difficult to use, the process is too manual

Organizational problem: You need FEA expert Manufacturing expert CAD expert Product engineering specialist

Resources are not deployed where decisions are made (“If you have CAD, It’s too late” – Matthew Loew)

Translation into CAD is very difficult and time-consuming

Redesign is required, for constraints are missing in TopOpt

Resulting designs cannot be manufactured

Issues with Traditional Topology Optimization


The design documentation(CAD) step is completely broken

Developers should be cognizant of the entire process: Product requirements → manufactured, documented, product

The overall process should not require human intervention

Much innovation is required for lattice structures in CAD and CAE systems

Some Closing Comments (1)


?Discussion, Questions and Answers?


We have to get beyond the issue of being able to design parts we cannot manufacture

We should not get stuck with parts we can manufacture but not design

Generative Design and Additive Manufacturing will prove to be incredible tools to enable innovation

End users should strive to understand these technologies and processes, and leverage them to maximum advantage at the earliest possible stage

Some Closing Comments (2)


CIMdata workshops and conferences

ASSESS – Nov 1 - 3, 2017, Potomac, MD (near DC)

COFES – April 12 - 15, 2018, Phoenix (Scottsdale), AZ

Topology Optimization Round Table – February 10 - 11, 2018, Washington, DC

User conferences: Autodesk Siemens PLM Software Dassault Systèmes Simulia Altair …

Further Resources - Conferences


"I firmly believe that Topology Optimization (TO) with additive manufacturing will revolutionize the way many products are developed and engineered. Not only are the TO design geometries beyond the capability of a human designer to produce, but the design effectiveness is almost always superior. And it’s not just a point design. The explorable design space starts exploding. Adjusting prioritization of different load cases, different physics, including uncertainties, adjusting for manufacturing constraints on various machines and even creating and using custom materials via lattice-type metamaterials – this all becomes part of a viable design exercise. And frankly, we are just beginning to understand the power of this transformation."

Ted Blacker Says


"Some, like me, believe that this move to Generative Design will revolutionize product design and development. In a relative sense, computing has become infinitely fast and vanishingly cheap, so we can now easily use heuristic brute-force methods to search for optimums. Also, Additive Manufacturing (3D Printing) promises the capability to make parts that were previously infeasible to produce."

Keith Meintjes Says


?Discussion, Questions and Answers?


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