But
what if instead of taking these steps to come up with a few designs, you could
have thousands of designs to choose from—all of which fit your stated
parameters? This would expose possibilities that you might never have imagined.
This
is where generative design comes in. Think of it as "another brain in the
design studio"— a computer program that uses algorithms to
explore and populate the design space. As shown in the image below, you might
end up with something fairly standard, or your chair may end up looking like
something straight out of the Museum of Modern Art, while meeting all of your
design requirements.
What if a CAD system could generate
thousands of design options that all meet your specified goals? It’s no longer
what if: it’s Autodesk Project Dreamcatcher, the next generation of
CAD. Dreamcatcher is a generative design system that enables designers to
craft a definition of their design problem through goals and constraints. This
information is used to synthesize alternative design solutions that meet the
objectives. Designers are able to explore trade-offs between many alternative
approaches and select design solutions for manufacture.
Recent advancements in Artificial Intelligence (AI) and the simulation of complex phenomena have enabled software to play an active, participatory role in the invention of form. Autodesk Project Dreamcatcher is an experimental design platform with focused research probes into generative design systems.
Recent advancements in Artificial Intelligence (AI) and the simulation of complex phenomena have enabled software to play an active, participatory role in the invention of form. Autodesk Project Dreamcatcher is an experimental design platform with focused research probes into generative design systems.
Figure 1: The Autodesk Project Dreamcatcher Workflow.
Figure 2: Autodesk Dreamcatcher example designs
for a chair.
The Dreamcatcher
system allows designers to input specific design objectives, including
functional requirements, material type, manufacturing method, performance
criteria, and cost restrictions. Loaded with design requirements, the system
then searches a procedural synthesized design space to evaluate a vast number
of generated designs for satisfying the design requirements. The resulting
design alternatives are then presented back to the user, along with the
performance data of each solution, in the context of the entire design solution
space. Designers are able to evaluate the generated solutions in real time,
returning at any point to the problem definition to adjust goals and
constraints to generate new results that fit the refined definition of success.
Once the design space has been explored to satisfaction, the designer is able
to output the design to fabrication tools or export the resulting geometry for
use in other software tools.
Define: Tools for Defining Problems
Dreamcatcher's
problem definition is a format for designers to describe design problems.
Through pattern-based description, solutions become modular and accretive,
thereby expanding the quality and number of alternatives that are searched in a
Dreamcatcher design session. The Dreamcatcher design knowledge base, created
through machine learning techniques, is a classified index of pre-existing
objects that perform functions, or satisfy constraints, similar to those the
user has defined in their problem definition.
Diversifying Input Modalities
Mimicking the
variety of reference material in a typical design brief, in Dreamcatcher the
designer explicitly and implicitly documents goals and constraints through a
number of input modalities including natural language, image inference and CAD
geometry. An individual or team may manipulate the problem definition through
these multiple modes of input and verify or modify the inferred changes to the
problem definition document. Focused efforts on modeling problem definitions
and performing design synthesis on full system models rather than individual
parts is an active area of investigation for the team.
Figure 3: Topology Optimization (TO) go through the Bike Frame and provide a surprisingly new design freedoms.
Generate: Shape Synthesis
The Dreamcatcher
team is developing several, purpose-built design synthesis methods that
algorithmically generate designs of different types from a broad set of input
criteria. Synthesis objectives include structural, thermal and fluid physical
requirements. Dreamcatcher's design synthesis methods compete against
each-other to solve problems most effectively through Dreamcatcher's
high-performance computing servers. A focused research effort into
incorporating manufacturing constraints for various methods of fabrication are
incorporated into the design synthesis process itself, so that only
manufacturable designs are returned to the design team. The Dreamcatcher system
enables designers to truly leverage an emerging class of manufacturing tools
that release designers from hundreds of years of predicating design decisions
on tool based constraints.
Advances in Cloud-Based Computing and Optimization
Through a
purpose-built, scalable and parallelized cloud computing framework code-named Saturn,
Dreamcatcher is able to generate and evaluate solution sets with complexity
well beyond that of Generative Design Systems of the past. Saturn provides the
high-performance computing infrastructure necessary to run the computationally
intense optimization and analysis engines, including multi-physics simulations.
Figure 4: SolidThinking Inspire design generation flow.
Explore: Design Space Visualization
After a number
of solutions have been computationally generated from a problem definition, the
Dreamcatcher design explorer presents to the user a set of possible solutions
and their associated solution strategies. This user interface provides a sense
of the shape of the valid design space and variable interactions. It also
assists users in building a mental model of which alternatives are high performing
relative to all others in the set. Once the solution has been adequately
explored, the designer can modify the problem definition to iteratively
generate more relevant solutions.
Traditional
optimization workflows like that of the NASA ST-5 antenna are 'bottom-up' where
a design space must be defined by the user and then searched by a genetic
algorithm or similar optimization function. By contrast, Dreamcatcher uses a
'top-down' approach where higher level goals are specified. This is the major differentiation between design optimization tools and Dreamcatcher's exploratory
design synthesis process.
Figure 5: Antenna bracket, redesigned using SolidThinking Inspire.
Arguments for
the incorporation of AI into design often default to concerns around replacing
the human designer. While many elements that are commonly modeled from scratch
such as brackets, adapters and stiffeners may be created more effectively by a
system such as Dreamcatcher, complex elements and aspects that are difficult to
quantify will require new types of interaction to leverage human intuition and computational
rigor in partnership. Dreamcatcher is pioneering new methods for interactive
synthesis and optimization with industry leaders from the automotive, aerospace
and manufacturing fields.
So what exactly Generative Design is?
Generative
design leverages machine learning to mimic nature’s evolutionary approach to
design. Designers or engineers input design parameters (such as materials,
size, weight, strength, manufacturing methods, and cost constraints) into
generative design software and the software explores all the possible
combinations of a solution, quickly generating hundreds or even thousands of
design options. From there, the designers or engineers can filter and select
the outcomes to best meet their needs.
Imagine if
instead of starting a "Drawing" or "CAD design" based on what you already know or
ideas that are in your head, you could tell a computer what you want to
accomplish or what problem you are trying to solve. For example, say you want
to design a chair. Instead of drawing two or three options (maybe 10 if you’re
really creative), you can tell the computer you want a chair that supports X
amount of weight, costs X much, and uses X material. The computer can then
deliver hundreds, if not thousands, of practically and easily manufacturable
design options that all meet that criteria and are likely options that you
could not conceive on your own. That’s the power of generative design.
Figure 6: Generative design replicates natural world's
evolutionary approach with cloud computing to provide thousands of solutions to
one engineering problem.
What is Generative Design not?
Generative
design is software that augments an engineer and uses the power of cloud
computation and machine learning to explore a whole set of new solutions. It
expands the engineer or designers known universe of valid solutions to their
design challenge. By contrast, many of the technologies that masquerade as
generative design - topology optimization, lattice optimization, parametrics or
similar technologies -- are focused on improving a preexisting design, not
creating new design possibilities like with generative design. The confusion arises because the inputs to generative design are
similar to the inputs to many optimization tools. However, generative design
produces many valid (high performance yet cost-effective) designs or solutions
instead of one optimized version of a known solution.
In addition to creating entirely new solutions, another area
where generative design differs and stands out is that it takes
manufacturability into account. That means the process of testing products and
going back to the drawing board is drastically reduced. Traditional
optimization focuses on refining a known solution, which usually involves
removing excess material without any notion of how something is made or
used. Additional modeling, traditional simulation and testing are then
required steps at the end. With
generative design, the simulation is built into the design process. You can
specify manufacturing methods like additive, CNC, casting, etc. at the outset
and the software only produces designs that can be fabricated with your specified
manufacturing method. Or you can explore designs for multiple manufacturing
methods.
Figure 7: On
the top, the preconceived bracket design from the 2013 GE jet engine bracket
challenge is refined using traditional
topology optimization. Because there is no awareness of manufacturing
processes, it will need to be remodeled again ‘by hand’ in CAD software.
On the
bottom, Autodesk Generative Design software uses attachment points, strength
requirements, weight, materials, and manufacturing method as constraints to
produce multiple geometric solutions for the bracket. There is no preconceived
geometry as a starting point. In this case, generative design produces 30
design options while topology optimization offers one.
Another often
overlooked benefit of generative design is the ability to consolidate parts. Because generative design can handle a level of complexity that is impossible
for human engineers to conceive – and because additive manufacturing can enable
the fabrication of the complex geometries that generative algorithms often
produce – single parts can be created that replace assemblies of 2, 3, 5, 10,
20 or even more separate parts. Consolidating parts simplifies supply chains,
maintenance and can reduce overall manufacturing costs.
With its ability to explore thousands of valid design solutions,
built-in simulation, awareness of manufacturability and part consolidation, the
reality is that generative design impacts far more than just the traditional
notion of design. It’s really about the entirety of the manufacturing process.
In some ways, you could argue ‘generative manufacturing’ would be a more apt
term. Because of its
expansive impact on the manufacturing process, generative design delivers a
leap forward in real-world benefits. It can lead to major reductions in cost,
development time, material consumption and product weight.
Airplane manufacturer 'Airbus' used generative design to reimagine an interior partition for its A320 aircrafts and came up with an intricate design that ultimately shaved off 45 percent (30kg) of the weight off the part using 3D printing in a high-tech alloy called “Scalmalloy”. That weight decrease will result in a massive reduction of jet fuel consumed and a reduction of hundreds of thousands of tons of carbon dioxide emitted when applied across its fleet of planes -- equal to taking 96,000 passenger cars off the road for a year:
How 3D-Printing works?
Capabilities of A 3D-Printer.
Topology Optimization ≠ Generative Design
Topology
optimization is often mentioned when discussing generative design, but the
terms are not interchangeable. Topology optimization is an aspect of generative
design, and involves designing based on various stress points to reduce the
amount of material used and thus increase the speed of production while
reducing material costs and weight. Topology optimization uses an existing
design, whereas generative design can involve inputting more general values and
building the design from the ground up. ProtoCAM
Engineering Manager, Ed Graham, has his own way of remembering the difference
between generative design and topology optimization:
“I always
think of the human body when it comes to the generative design approach;
through generations, the human body has evolved into what we are today,
although every body type has its own constraints to fit the exact body type,
i.e. short people, tall people etc. Topology optimization is based more around
science, and involves engineering based on fixed constraints of a specific
design,” Graham says.
Difference between Topology Optimization and Generative Design Video Link: https://www.youtube.com/watch?v=uqWEwtJuRa4
Topology Optimization asks, what aspects can I remove to optimize this design? Whereas
as Generative Design asks, what can I create based on these specific
constraints and requirements?
How generative design exactly works.
The generative design process starts with a designer defining a
bounding/design area, connection points and obstacles, as well as a variety of
constraining parameters. An example of the first three can be seen in Figures 8, 9 and 10, where these parameters are defined for a motorcycle swingarm
design. Optimization is based on desired materials, manufacturing technologies
(method of production), temperature tolerance, cost, and strength of the part
and its ability to withstand specified forces.
Figure 8: Bounding space for the swingarm.
Figure 9: Connection points for the swingarm
(where the part connects to the wheel and the motorcycle).
Figure 10: Obstacles for the swingarm (wheels and
chain) where a chain is placed on both sides to create a symmetrical result.
Figure 11: Resulting design for motorcycle swingarm.
Normally,
a designer would have to spend several hours trying to calculate whether their
proposed design would fit these constraints. In contrast, this is done by
default using generative design and all of the resulting designs will fit the
defined constraints, and often exceed them. Because of this effective
automation of tasks through software, there is room for the designer to play
around with aesthetics of the part and invest more time into exploring creative
and effective designs.
Optimization through constraints.
So,
what about all of these constraints? You might not care about parameters like
temperature, flex or weight to a great extent for a chair. But you would for
brackets, winglets or gas turbines in planes, or bridges that you walk and
drive over. When airplanes fly, for example, every ounce of weight translates
into huge incurred fuel costs. Parts must be able to withstand tremendous
forces and large variations in temperature, with proper heat dissipation.
Manufacturers
are always trying to optimize their parts to account for weight and harsh
conditions. General Electric, in partnership with GrabCAD, went so far as to
launch a 3D-Printing Design Quest. They challenged the public to redesign a
metal jet engine bracket, making it 30 percent lighter while preserving its
integrity and mechanical properties like stiffness. The winning design sheared
off 84 percent of the weight of the original bracket, taking the total weight
from 4.48 pounds to just 0.72 pounds (Figures 12, 13).
Figure 12: GE’s original bracket.
Figure 13: GE’s redesigned jet engine bracket.
Using generative design, companies could duplicate this
crowd-sourcing effort internally, using the software program algorithms to
arrive at numerous solutions that fit desired parameters.
Generative design and Additive Manufacturing.
Using Additive Manufacturing or 3D-Printing allows for even more
design possibilities. This can be done by varying the parameter of
“manufacturing technology” in Dreamcatcher. Additive manufacturing technology
has been evolving at a rapid pace. It is now to the point of producing parts of
industrial quality due to the likes of FIT AG9 for metals and Carbon3D10 for
polymeric plastics.
Autodesk also offers a generative software product called
Within-11, which makes use of additive manufacturing capabilities to create
lightweight, latticed designs for automotive, medical implant, aerospace and
industrial equipment applications. These designs would not be producible by
traditional means. Using Within, engineers were able to create a lightweight,
load-bearing engine block that had better heat dissipation and superior
performance (Figure 14); a lightweight roll hoop for Formula One racing cars
(Figure 15); a customized implant for cranioplasty (Figure 16), and
micron-accurate rough lattice surfaces for medical implants to aid fixation
with bone.
Figure 14: Automotive: Load-bearing
engine block.
Figure
15: Automotive: Lightweight roll hoop.
Figure
16: Medical Customized implant for cranioplasty.
Figure 17: Autodesk Medical Implant containing lattice structures.
Additive
manufacturing combined with the Within software has been even more helpful when
it comes to traditional injection molding. Figure 18 shows the traditional
solid mold on the left side, and a re-designed latticed mold on the right, with
better simulated heat dissipation properties. In addition, this mold is more
lightweight and requires much less material to produce.
Figure 18: Injection mold optimization using
Autodesk Within.
With the technology constantly evolving and reshaping itself
(pun fully intended), the number of design possibilities will explode and
manufacturing efficiency will further improve. Think lighter, faster planes
with optimal engine heat dissipation. Self-building and self-repairing bridges
that look organic and last longer. Faster bone growth thanks to
biodegradable, bone growth-stimulating optimized and 3D printed implants. And
this is just the beginning.
I have taken two important generative design case studies which are to be discussed in the upcoming headings. These studies will help us in understanding the implications of generative deign into normal production schedule so that design optimization for the near future will be at different level.
I have taken two important generative design case studies which are to be discussed in the upcoming headings. These studies will help us in understanding the implications of generative deign into normal production schedule so that design optimization for the near future will be at different level.
Case Study 1.
Airbus Cabin Partition.
Airplane manufacturer 'Airbus' used generative design to reimagine an interior partition for its A320 aircrafts and came up with an intricate design that ultimately shaved off 45 percent (30kg) of the weight off the part using 3D printing in a high-tech alloy called “Scalmalloy”. That weight decrease will result in a massive reduction of jet fuel consumed and a reduction of hundreds of thousands of tons of carbon dioxide emitted when applied across its fleet of planes -- equal to taking 96,000 passenger cars off the road for a year:
(1) The design that was
arrived at by using an algorithm based around mammal bone growth.
(2) Stress testing compared
to existing partitions.
(3) The 3D-printed parts
being cleaned up.
(4) The resulting
partition, which is 45 per cent lighter than the existing panel.
Figure 19: Airbus Cabin Partition.
Figure 20: Cabin Partition optimized using generative design.
Figure 21: Airbus Cabin Partition Generative Designed and Successfully.
Figure 22: Comparison of Building Limits in EOS M400 and Concept Laser M2 machines.
Project Video Link: https://vimeo.com/151334118
Case Study 2.
X VEIN-The Life Saving Drone.
Yuki Ogasawara and Ryo Kumeda, aka team ROK, were just 15 years old when the Great East Japan earthquake and tsunami caused immeasurable destruction and damage on March 11, 2011. Like many of the country’s citizens, the two friends considered how they could help others in the disaster’s wake, starting them down a creative path toward drone development.
Drones were just gaining attention, and Ogasawara and Kumeda sought to apply this emerging technology toward disaster efforts. They had already won the Fighting Spirit Prize at the National High School Programming Contest (with a robot arm controlled by a smartphone) and taken seventh place in the RoboCupJunior Japan Open rescue division, and they were ready for the challenge. So in 2012, team ROK began developing a multicopter drone. And in 2015, they won the multicopter division of the National Student Indoor Flying Robot Contest.
Building on that success, team ROK went to Maker Faire Tokyo 2016, where they exhibited X VEIN, a drone built for disaster conditions and search-and-rescue missions. Featuring extended flight times, a reinforced frame, and propeller guards to prevent damage from crashes, X VEIN impressed visitors with its X-shaped body and lattice-structure design reminiscent of veined dragonfly wings.
But X VEIN’s appearance at Maker Faire Tokyo might not have happened if not for an event held at DMM.make AKIBA, a Tokyo hub of the maker community. There, Ogasawara and Kumeda first encountered generative design software and knew it would be essential for developing a drone with sufficient body strength, lightness, image-capturing capability, and safety features.
Figure 23: X VEIN had full 3D-Printed body with Generative Design wings.
Generative Design Breakthrough:
“There are many reasons existing drones are not used in disaster-hit areas, including their lack of safety features, their size and weight, and the low potential for customization,” says Ogasawara.
Clearly, a tool that can solve for those requirements is key to any drone-development process. Generative design provides computer-generated schematics and structural analysis optimized to fulfill predetermined conditions, which can then be output to a 3D printer. This allows for lighter designs than previously possible with conventional manufacturing methods—essential for producing a drone that would meet the necessary weight requirements.
“For a drone to hover in midair, the lift it generates must exactly match its own weight,” says Ogasawara, who is responsible for X VEIN’s mechanical design elements. “Variations of even 5 percent of overall weight change how operators must control the drone. It is crucial we make our drone as light as possible.”
Figure 24: Fusion 360 illustrating Generative Design wings for X VEIN.
X VEIN Gets Its Wings:
Currently a student at Saitama University’s Department of Electrical and Electronic Systems, Ogasawara joined hardware startup Exiii as an intern in early 2016. At Exiii, he worked with chief creative officer and designer Tetsuya Konishi. By April, they had completed design sketches for the X VEIN concept. The generative design-based lattice was integrated into the drone’s body, leading to the unit’s current design.
Figure 25: X VEIN in operation mode.
On the modeling of the drone’s body, Ogasawara says: “The design had a lot of free-form curves, which are hard to translate from sketches to a 3D model. Through Wacom, we were given access to a Cintiq pen display tablet. Using it to build up a model in Autodesk Fusion 360 with input based on our sketches made it possible to follow our design while smoothly re-creating it in 3D space.”
“With a 2.4 GHz frequency used for control, we have an operational range of about 500 meters without any obstructions,” Kumeda continues. “Current laws in Japan require operators to stay within visual contact of their drones, so our effective range comes to about 100 meters.” The drone’s camera is mounted on a gimbal, isolating it from vibration and tilting, stabilizing the image. The images can be viewed in real time using a smartphone, accessing areas made impassible by disaster damage, confirming conditions, or performing other tasks. The drone could also carry thermographic and infrared-imaging equipment to locate survivors. Because most of X VEIN’s components are 3D printed, replacement parts also can be procured on-site—a huge advantage in disaster situations.
Figure 26: Team ROK's Yuki Ogasawara (left) and Ryo Kumeda (right) won the the National Student Indoor Flying Robot Contest with the blue drone on the right.
How 3D-Printing works?
Capabilities of A 3D-Printer.
Housing Crisis and 3D-Printing.
