Description
Key Learnings
- Discover all of the new generative design features released in Fusion 360 in 2021
- Discover the future plans for Generative Design workflows in Fusion 360
- Learn about the future plans for Generative Design physics in Fusion 360
- Take a deep dive into key generative design improvements
Speakers
- Michael SmellMike is a Sr. Product Manager on the Fusion 360 team at Autodesk. He has been working on Fusion 360 for nearly 7 years and is currently responsible for the Generative Design portfolio. He has previous experience as a Technical Account Manager in Autodesk’s Manufacturing Named Accounts program, where he was working with customers to help them identify and solve business challenges with Autodesk solutions. Mike has spent nearly 17 years in the CAD and CAE industry, starting his career at Algor, Inc. in 2006, eventually being acquired by Autodesk in 2009. Mike holds a bachelor’s in Mechanical Engineering from the Pennsylvania State University, a master’s in mechanical engineering from the University of Pittsburgh, and has completed a certification for Machine Learning in Business from the MIT Sloan School of Management. Mike has been a regular presenter at Autodesk University since 2009.
- Peter ChampneysPeter is a mechanical engineer with over 7 years of experience working with generative design. Based out of the Autodesk Technology Center in Birmingham, UK, he has worked on a projects from a wide variety of industries including automotive, aerospace, consumer products and construction.
- HHHeath HoughtonHeath Houghton is a Professional Services Consultant for Autodesk, specializing in Generative Design, structural simulation and fluids and thermal simulation. Heath helps customers meet their design and manufacturing goals by maximizing the potential of Autodesk's generative and simulation platforms. Prior to working in consulting services, Heath served as product manager for fluids simulation products. As Product Manager, Heath guided the development efforts and roadmap decisions for flow and thermal simulation projects. Heath joined Autodesk with the acquisition of Blue Ridge Numerics CFdesign. He was in a technical role with Blue Ridge Numerics for several years and he continued in that role with Autodesk before transitioning to Product Manager, then over to consulting services. Heath has over 20 years of experience with both fluids and structural simulation tools. In his spare time, Heath enjoys archery and training his bird dogs.
MIKE SMELL: Hello, and welcome to Autodesk University. And welcome to Generative Design in Fusion 360, Another Year Older, Another Year Better.
I'm Mike Smell, Senior Product Manager for Generative Design in Fusion 360, and today, joining me is Heath Houghton and Pete Champneys. Guys, please do a quick introduction.
HEATH HOUGHTON: I'm Heath Houghton. I am the Product Manager for all things fluid simulation, so that includes our Generative Design products, electronics cooling, and our Autodesk Safety Product.
PETER CHAMPNEYS: My name is Peter Champneys. I work as a technical consultant, so I spend my time working with various Autodesk customers, helping them take Generative Design technology and apply it to their business requirements.
MIKE SMELL: Thanks, guys. Let's go ahead and jump into it.
So today in our conversation, we will be talking about three main topics. We'll start off by doing a 2021 Year in Review so that we can see all of the progress we've made since the last time we were together at Autodesk University.
The next thing we'll look at is Pete will take us through some of the customer projects that he's been working on this year.
And then lastly, Heath and I will team up to talk about the future of where we see Generative Design going for Fusion 360.
Now, getting started with our Year in Review, I want to lay out some of the guiding principles that we think about when we develop software.
Generative Design in Fusion 360, we focus on providing the mechanical engineer with automated design tools intended for mainstream product development. And this is really about design exploration and finding new solutions. We base that on exploring geometric connections, performance objectives, manufacturing considerations, and material selections.
And when we think about the types of features that we add to the product, it's really about ensuring awareness of how the solution can help you, making sure that it's accessible and easy to use for all folks in the design and engineering process. And then lastly, that it has a broad range of applicability so that it can help you solve as many design challenges as you may face.
Let's start by looking at what we did in December of 2020, which was shortly after the last time we were together at Autodesk University. One of the things we heard from many of our customers was it was too hard to work in a part-based context for Generative Design. So we went in and enabled remote forces and constraints which are common things that users of simulation products are familiar with when defining their performance requirements.
So this is part of our Experimental Solver and Features Preview. And this is good for when we know that our load locations are not directly on the preserves. It's also good for when we have boundary condition locations that are not directly on the preserves. We would be understanding both of those being on other parts of the assembly. And it's also good for the scenario where we need additional flexibility and boundary conditions so that we can accurately represent the physics. A good example of this is if the preserve is supported by a universal joint.
The next improvement we made in December was around additive manufacturing. We'd heard from many of our customers that our additive manufacturing outcomes weren't meeting their expectations both from a printability point of view, but also from a shape quality point of view.
So what you'll see here in the image is that we made some progress, where we're making our new additive results look a bit more like our unconstrained results while we're still focused on minimizing support material and decreasing weight.
So the overall benefits that we were able to achieve with this project is first and foremost, we got much better shape quality out of this. Things are far better at meeting customer expectations.
Next, we've improved the ability to meet overhang angle and minimum thickness definitions. So many of you may be familiar with some of our older additive outcomes where there was a very pronounced step pattern in them. That's no longer going to be the case. We've also found that these solutions are now generally lighter than all of our previous additive outcomes with our old strategy.
And then lastly, as we said, we've moved away from forcing ourselves to create self-supporting parts. We were doing that at the expense of the performance, the weight of the part, as well as the shape quality. And now purely just focusing on minimizing support material required. And we've seen good progress with this across all sorts of different additive manufacturing techniques.
The next thing we did in December was focused on how do we help you work through the design exploration and decision-making process in the Explore environment. Here we've added the ability to tag an outcome as a favorite.
Now this is an important feature because it helps you take advantage of all of the intermediate iterations that we create as part of the exploration process. So now, if there's an intermediate iteration that you like for aesthetic reasons-- it's a little bit heavier looking, it's a little bit chunkier-- you can now tag that outcome as a favorite. It will create a design preview so you can see a nice rendered shape. It will provide you cost estimation information. It will provide visual similarity grouping. And it's also going to allow you to use this outcome with all of the other outcomes. So in things like the comparison view or the scatterplot views.
Now, moving forward to our March release. We wanted to add a new command called "Obstacle Offset." And the reason we did this is because we heard from a lot of users that typically their obstacles were other parts of the assembly or surrounding components to the thing that they were trying to design.
Oftentimes, it makes sense to create a little bit of extra clearance between those parts and the things that you're designing. Well, that would require you previously to go in and modify other parts of the assembly simply for the purpose of Generative Design.
So we wanted to give the user a way to do that virtually. So now, in the Generative Design setup process, you can pick the obstacles that you've got created and add an offset value to them. So the Solver will treat those obstacles as bigger, but it won't require any CAD changes.
The next thing that we did in March, much like our work in December, is continuing to build out support for working in the context of an assembly. So now we've added the Remote Load Type, which much like remote forces, allows you to apply moments in locations that exist where it's not directly on the preserves. And it also allows you to apply moments that have axis of rotation that are not aligned with the surface of the preserve. So in this image you see here, we're applying a moment about the vertical axis, which is orthogonal to the axis of the highlighted surface. This feature, as well, is part of our Experimental Solvers and Features Preview.
Moving forward to April, we wanted to make it more obvious to users that we have a seven-day extension trial that allows for unlimited use of Generative Design and consumption of outcomes. And we find that this is a great opportunity for users to learn how to use Generative Design, get started with tutorials and learning exercises without having to make a commitment. We brought this from the extension manager to the workspace. So now, when you enter the Generative Design workspace, if you're eligible for a trial, this will be presented to you and you can activate it directly as part of your workflow of getting into the workspace.
Another feature that we released in April is continuing to build on our improvements around the moment workflows. We wanted to get complete parity with the moment workflows that we have in our simulation workspace so that when we take a Generative Design outcome into simulation workspace for further validation, we have clear parity and we know exactly how the loads are going to translate from one environment to the next. So similar to the remote loads, we're now able to, again, apply a moment to a surface at an orientation that is not necessarily aligned with the axis of the surface. Again, another feature that's part of our Experimental Solvers and Features Preview.
Moving forward to May. Back on the topic of results, exploration, and decision-making, we've added a transparent view to the outcome view in Explorer. And the primary reason for doing this is that with our new Experimental Solver technology, there are scenarios where we will create hollow shapes.
So in the example that you see on the screen there, without the transparent view, you'd think it looks like a generic cone-based shape that isn't interesting. However, what we've done is we've created a void, in an internal void, that has an appropriate wall thickness to support the loads. So now, with the transparent view, we can more easily evaluate the shape detail of outcomes. And it'll help you identify whether or not there are internal voids in your structures.
The next thing we did in May, continuing to build out our Experimental Solvers and Previews Feature, is we've added the ability to do two-axis cutting, 2 and 1/2 axis milling, and additive manufacturing outcome constraints with that Solver Preview. To that point, we were limited to doing unrestricted three-axis and five-axis. So this will allow you to create additional outcomes to explore for meeting your design needs.
And I think what you're starting to see is that we're continuing to mature our Experimental Solvers and Features Preview. And this is our test bed for bringing all of our new work to market, which will ultimately become our new default solutions.
Moving into June. Major changes on how users access Generative Design were released. And this is all around how our business model allows you to experience and pay for Generative Design.
We had some pretty significant price changes. Starting with the subscription model, or the extension, we reduced the price to $1,600 US dollars per year, and we reduced the monthly subscription to $200 US dollars.
In the pay per use mode of using Generative Design, we changed the Generate price to 33 Cloud Credits. However, we removed the price to convert outcomes to usable B-reps, and we've also enabled that any outcome generated in Generative Design from the time of commercialization could also then be converted to a usable B-rep at no additional charge.
We heard from a number of our users that the concept of having a pay point at time of generating a study and then a pay point at the time of consuming an outcome was just not meeting your expectations, too difficult to understand, and was really clumsy in the way that you wanted to work with the product. So we went in and rethought how we could deliver our business model, and this will be the new process going forward.
The other thing that we did with this new pricing scheme is we wanted to make it extremely obvious for our users to understand when they should use Cloud Credits and pay per Generate versus when they should subscribe. And we built this new pricing model on the concept of four studies per month. If you are going to do four studies per month, or 48 or more studies per year, it is more beneficial for you to go with our subscription business model. If you're not going to do an average of four studies per month, or less than 48 studies per year, then you should continue to use the Cloud Credits in the pay per use business model.
So the benefits that we hope to achieve with this was we made it much easier for you to determine the best payment model, we've simplified the transaction down to a single paywall, and we've taken away the charge to create editable outcomes, and we've unlocked all the outcomes that you've ever created with Generative Design.
Moving ahead into our July update. Back on the topic of looking at results, we've added the ability to export outcome property data from Explorer into a CSV. This is a much more simple workflow than copying and pasting data from Explorer into Excel. And this supports the steps required to do additional decision-making and find the right outcome that solves your design challenges.
Another update that came along in July is around editability and creation of additive constrained outcomes. So we've added the ability for the user to define symmetry in an additive manufacturing constrained design. You'll see here in the upper right, we've got a symmetric design. This requires that our preserves and obstacles are symmetric, but it does not require the loads and constraints to be symmetric. So a good way to drive an aesthetic behavior that you're after into the shape.
The other nice feature associated with this is the definition of symmetry carries all the way through to the outcome. So if we need to make tweaks to that geometry, add some refinement to it, it's going to behave when editing in the same symmetric definition that you used for the outcome generation.
And then the last thing that we've deployed here in 2021 leading up to AU was in our August release. And that was bringing the capabilities from the simulation workspace around defining a point mass into Generative Design.
So again, now we've got more and more parity with our simulation workflows. We've expanded the applicability for the types of solutions that can be solved in Generative Design. And this is primarily used when folks have gravitational loads that are driving critical load cases.
Another way that to think about this is if you've got an external component hanging off of the thing that you're trying to design, you can represent the weight of that component with the point mass at its center of mass so that we've got an accurate representation of how that system behaves. Again, this is another feature that's part of our Experimental Solvers and Features Preview.
So that was a lot. That was a lot in 2021, and a few carryover things from 2020 from the time we last spoke at AU 2020. Given that, I hope you can see we've made a ton of progress in the solution.
Pete's now going to talk to you about some of the customer stories that he's been off building leveraging all of this new technology.
PETER CHAMPNEYS: So in the past year, there have been so many great stories of companies using Generative Design. Now, a lot of those, we're not allowed to talk about, but there are some where we are. And I've selected just a couple of customer stories which I believe really highlight how those development goals, which Mike was speaking about at the beginning-- so increasing awareness, increasing accessibility to the tool, and increasing the applicability of the tool-- can be seen to be having an impact not just on the way that people are using Generative Design, but also on the type of company that is able to start seeing value from the Generative Design approach.
So the first customer story is from the world of first-person view drone racing. So this is a sport where racers wear a headset with camera footage from a racing drone live streamed to them so that they can race from a drone's eye view. It might not be a sport you've ever heard of, but it's rapidly growing and expected to reach a market size of $2 billion in the next five years.
So the drones themselves are really impressive pieces of kit. They hit top speeds of over 100 mile per hour. They can pull 20G on the cornering, and they accelerate from naught to 60 in just half a second.
So one company that's trying to push those limits even further is Volve, and they've created the world's first fully AI designed racing drone. So instead of focusing on a single component, they've built an AI algorithm which is able to optimize the entire system. So for a racing drone, that means considering which motor is best, which battery is best, and then figuring how all that should fit together to get from A to B as quickly as possible.
So their algorithm is able to explore millions of potential permutations and combinations to come up with an ideal system which can even be tailored to specific pilot flying styles. And once they've created this ideal system, they then take this data and use it as the starting point for Generative Design in Fusion 360. So they're using Generative Design to create the geometry for the drone frame, the key element which holds everything else together. So by using Generative Design, they can maximize the stiffness of the drone frame for the cornering and acceleration maneuvers of the drone while continuing to keep it as light as possible.
And one of the most important aspects of this design process are the 2.5-axis CNC machine and constraints. These constrain the design outcomes so that Generative Design comes up with outcomes which can be manufactured using traditional method to drone frames, which is routing out of the carbon sheet. So because Generative Design is building and manufacturing awareness, such as maintaining minimum [? fillets ?] size for a given cutting tool, they can have confidence that the designs they come up with are ready to take straight to production.
So another company that have been making great use of Generative Design to streamline their manufacturing processes are Print City. Print City are a unique additive manufacturing facility that serves as a student workshop and lab, an in-house service bureau and contract manufacturer for a wide range of local and national customers. So they service industries from fashion and textiles to chemical and engineering companies to support them with 3D printing, 3D scanning, and prototyping requirements.
So one of their most popular printers is a large scale FDM machine, which feeds in filament through an overhead tube. You can see that on the screen here. So their original set-up weighed over 5 kilograms, and this was causing all sorts of issues with the 3D printing process. So they needed to come up with a lighter, more versatile real design that was still strong. And they realized that Generative Design was a perfect tool to solve that problem.
So they started off by looking at outcomes with no manufacturing constraints applied whatsoever. So completely unrestricted designs just to see what the lightest design possible might look like. The designs that came out the other side were extremely lightweight, and they love the organic design that showed off what 3D printing is capable of.
But when it came to 3D printing this design outcome on their mark forged, they realized that they really needed so much support material in order to allow the design to be printed, even if they tried to print it in multiple parts. And what they found was that in the end, it would actually be using more material on supports than on the part itself.
So they had a great idea of what was possible. What they now wanted to do is get back into Generative Design, and this time apply those constraints for the manufacturing process. So they actually ended up using a combination of two and 2.5-axis manufacturing constraints to come up with a design that still had all of that intelligence built into it-- so a really lightweight, stiff design. But because of the simplifications applied by the algorithm, they could now print the design in sections without needing any support material whatsoever.
So if we compare those two designs side by side, we can see that the design of the manufacturing constraints applied, the part itself has a little bit more mass added to it. But because it doesn't need any support material to produce, they're actually using less than half the amount of material in the production process. And because of the manufacturing method they're using, that means that the time to produce and the cost to produce are also cut by the exact same amount.
So the last customer story I wanted to share is from the world of mountain biking. There have been a whole range of great success stories coming from the world of cycling and bike manufacturers, where companies have been picking up Generative Design to create really innovative new components and push the boundaries of what's possible.
One company who've been doing this are Starling Cycles. And Starling Cycles produce handcrafted steel frame mountain bikes. You can see one of them in action there. And their design approach is all about eliminating complexity and creating elegant bikes that simply perform. So their bike frames are produced by welding the various components together by hand.
And with their traditional design and manufacturing process, at first glance, they might not seem like a natural fit for a technology like Generative Design. But Joe McEwen, who is the founder of Starling cycles actually spent many years working as an aerospace engineer before starting Starling. And he's always been looking to marry the latest technology with the traditional approach where it makes sense and can provide value.
So they wanted to look at one component within their frame assembly, which was the main pivot, and see if they could make improvements to the design to reduce the time for assembly. So typically, they would use just a standard piece of tubing for the main pivot interface, but to fit this part in took a lot of steps. And they often had issues correctly aligning that component during the assembly process.
So using Generative Design, they started to explore what that component might look like if they replaced the existing set-up with a single casting, which could be produced by their suppliers in Taiwan.
So Generative Design was quickly able to produce design possibilities and outcomes, which Joe was really, really happy with. And again, the manufacturing constraints that they used were able to ensure that the parts were something which they didn't have to invest in complicated new 3D printing technology to produce, or which they could use their existing supply chain to manufacture.
So the new design that generative design came up with-- you can see on the screen here-- it takes just 15 minutes to assemble compared to the old set-up, which was taking them an average of more than 1 and 1/2 hours. So you can think, that's an enormous time-saving on every single frame that they produce moving forwards.
These stories represent just a few ways that customers are gaining some real advantages by starting to explore Generative Design. So in the first story, we saw an example of how companies are designing high-performing components like ultralight high stiffness racing drones. And like Volve, companies are also starting to explore how they can link up customer-specific usage data to Generative Design in order to create high value customized products.
Another maybe surprising use case for Generative Design is not just on end-use products and components, but on the many design elements that go into a manufacturing process. To jigs fixturing, and in this case a 3D printed spore holder are not necessarily end-use products themselves, but they're essential elements to design correctly, nonetheless, and play a key role in the overall manufacturing process.
And lastly, we saw how companies of all scales and sizes are starting to see how exploring design alternatives and letting Generative Design take them outside of the way that things have always been done can yield surprising and significant benefits to all aspects of their business.
So that's a look at what's going on today with Generative Design. I'm now going to pass you back over to Mike, who's going to talk a bit about some of the exciting things coming up on the horizon.
MIKE SMELL: Thanks so much for that, Pete. And I must say, that's a lot of amazing work that you've been working with our customers on this past year. I'm so excited. And to see all those, I think you summarized it great that there's a number of different applications where Generative Design makes a lot of sense. And hopefully what you see Heath and I talk about here as we look into the future of Generative Design, we'll see that that range of solutions and values that Generative Design can offer grow even larger.
Coming back to the thread that we've been keeping consistent throughout the presentation here, is understanding the guiding principles for development. So the things that you will see Heath and I talk about in the coming slides are continuing to push on ensuring awareness of Generative Design and how it can bring value to your design process, ensuring accessibility, and ensuring applicability to as many possible parts of the design and engineering workflow as possible.
We'll start off with the first thing that we will be working on this year, and this is bigger than just Generative Design. This is something we will be doing across the Fusion 360 and product design and manufacturing collection kind of ecosystems. And that's looking at how can we improve the Inventor to Fusion data exchange?
You saw us take some initial steps at improving these workflows with the release of Inventor 2022. We will be further building these workflows out to have an intent-based focus for Generative Design simulation and manufacturing. So now when a user is in Inventor, we will provide them very clear and guided workflows based on their design intent, or their usage intent, to bring their information from Inventor into Fusion 360 for purpose-built workflows.
The next thing that we will focus on as we continue to broaden the applicability of Generative Design is manufacturing. We will continue to focus on finding new methods that will influence the shape generation process so that there's manufacturing awareness in the outcomes that come out of Generative Design.
The first area that we're going to look at is additive manufacturing. You saw us do a bunch of work already in the past year to improve the quality of the shapes coming out. We want to take that one step further-- and not only looking at the performance of the design, the shape quality of the design-- but also the printability of the design. So we will look at how can we provide optimized part orientations so that the support material, support volume, all of the characteristics of the print process are improved with the design that comes out of Generative Design. So when you go to that printing process, printing prep process, not only do you have a high-performing design, you have a design that's going to be well-thought-out for production.
The next area that we will look at is around casting. Many of you may already know that we have die casting as a manufacturing constraint in preview. We want to continue to mature that solution, and we also want to broaden that solution to start to be able to offer solutions for sand casting. So over the coming year, you will see us make strides in this area.
And in the last area that we will look at is helping folks with really much more traditional design processes with things like weldments and sheet metal. So we've got some research going on now that looks at how can we start to do Generative Design on these thin structures, where we're looking at understanding that the downstream manufacturing processes will either be a nest of plates that will be cut out and ultimately welded, or sheet metal that will be [INAUDIBLE] formed. So hopefully, when we talk to you at AU 2022, we'll be able to demonstrate some progress in these areas.
Now the next two projects that Heath and I will talk about are really some of the lighthouse projects that we have going on in Generative Design for the foreseeable future. This first project that I'll talk about is really around expanding accessibility of Generative Design. And we're calling this project Generative Design for Modeling. And what we're looking to do here is to create a completely brand new workflow that's focused on rapidly exploring design alternatives, focused on geometry creation and connections. So our intent is to build a tool that feels exactly like a modeling tool. It responds like a modeling tool. It lives in the design workspace where you're doing your design modeling.
We want to focus on building the solution where minimum setup definition is required. so this is much earlier in the process where we're just trying to understand form and space envelope development. We haven't yet started to think about performance requirements and manufacturing requirements or material requirements.
We're going to provide multiple shape alternatives, as you can see here in the image. And in multiple shape alternatives, not only multiple shape alternatives by themselves, but also with construction methods. So similar to the Generative Design offering today, manufacturing constraints like 2- and 2 and 1/2 axis outcomes create outcomes that can be edited through standard Fusion modeling features like sketches, and extrudes, and combines, whereas our 3D solutions are typically edited using piece blind forms. So this Generative Design for Modeling project will create outcomes in a very, very similar way where you'll have full accessibility to the features that create these shapes so that they can be refined and edited for your needs.
And the last thing that we want to do with this project is create an on-ramp between shape creation through this Generative Design for Modeling project where we're taking our actions in the Designer [AUDIO OUT] bridge to get users from this early form development into more performance and objective-based design workflows in the Generative Design workspace that we know today.
So if we take a look at what some of these use cases look like, you'll see some of these designs look very familiar to things that you may see coming out of Generative Design. Some of the setups may even look familiar from some of our tutorial models, and some are then quite different. But really, what we're allowing you to do as the user is to simply tell us what places in space do you want to connect, where might we need to avoid putting material, and then we will go ahead and build you a number of different shapes that would fill that space.
So you can see here we've got outcomes that are connecting two faces, where maybe you would have used a loft in the past, or you may have needed to do multiple actions to get lofts to work in the past. You see here some more complex designs where we've got multiple faces that are being connected, and then you also notice some of the strategies where we've got parts that are very smooth looking, and then some parts that are more prismatic and basic looking where we're doing, again, standard Fusion modeling features.
Now, I'd like to take an opportunity to show you where we're at with this project. We don't have an exact timeline on when this will be delivered to the public just yet, but we've made good progress and we expect to have something available reasonably soon.
So here you are. You see we're in the design workspace, and we've got a new panel called Generate, which starts the Generative Modeling command. And at this point, the user is picking faces to connect. They have the option to select bodies to avoid. We have standard modeling operations like New Body, Join, New Component.
And then we'll click. We'll tell the system to go off and generate new shapes. So leveraging the power of the cloud and our Generative Design technologies that are powering Generative Design, we rapidly go off and create these various outcomes based on different algorithms for shape style, meaning more prismatic or more smooth.
And then we can iterate and look at each of these shapes, evaluate them for how they fit into the context of the assembly, and then take that design forward. You'll notice here once we choose an outcome, we can see the previews updating in the canvas, so it gives us a good feel for what these different alternatives look like.
Now, mind you, what you're looking at is pre-production code, so there's still some things that we're working out. But then you'll notice that in the timeline, we've got all the features just like it would if you created that thing manually. It can be edited and modified.
Further, if we say, hey, we'd like to go back and explore a different type of alternative. We can simply edit it, edit the Generative Modeling Command. We can choose one or the other forms and click OK. And it's produced in the canvas as a usable Fusion model.
So here, you know we're still pretty early in our design process with the generative modeling technology. We're still refining how the shape can be used, but here we're doing some standard downstream operations where we're putting nice, hard, crisp endpoints for our connections onto the model. So we've combined that in. So again, fully usable Fusion model.
We've got our new result, and at this point, we can then go start to look at like how well is this going to work in the context of our design. Does it meet our aesthetic requirements? Does it meet our form factor requirements? Does it meet our performance requirements? Maybe it does. So here we're going to derive it out into a new file. We'll jump over into the simulation workspace, and we'll conduct any of the traditional simulation workflows that we may be familiar with for validating the design of a part.
So here we'll go ahead and activate a modal frequency study. We'll throw some basic constraints on the model. And we'll start to do the first steps in understanding the performance of how this design might work based on our needs.
After we review the results, we can start to make some decisions. Is this a design that we can move forward with? Is this a design we need to modify? Is this a design where we need to go try a completely new shape type altogether, and maybe look more at a more prismatic shape? Regardless of the decision, we can come back over to the originating design and take any of the actions to modify the design from here forward.
So that's an early look at the Generative Design for Modeling project. I'd like to turn it over to Heath to talk about another one of our projects that's really going to change the scope and scale of what we can do with Generative Design.
HEATH HOUGHTON: Thank you, Mike. That's really exciting-- new feature sets that you've gone over. It's crazy how many things have come across the board in the last year, and that are coming to our customers over the next several months.
What I'd like to talk about is our expansion of Generative. In general, to date, what you've seen has been structural. What you get is a structural component basically from Generative, and we're adding new physics to the suite of capabilities within Generative to handle the physics of fluid flow.
And what we're doing is adding those new capabilities to design, using the same workflows and paradigms that we have within Generative today but in the context of, I want an efficient flow path. I want to minimize pressure drop. I want to do other engineering design objectives where the fluid flow-- so whether that be a gas or liquid-- impacts the performance of my parts.
So what we will do is initially focus, like we said, on efficient fluid path design. So getting from point A to point B, or multiple points A to multiple points B, in an efficient manner. So you're going to get as low of a pressure off as possible.
We are going to then add on additional engineering capabilities. In our first example of this, we'll be able to, for instance, balance the flow between multiple outlets, or bias the flow between multiple outlets. And that will be our first expansion of the engineering design objectives.
What you will get as a receivable is, at first, a fluid path geometry. So you will get the actual fluid volume that you can then use in downstream design or workflow. So if you want to make a [INAUDIBLE] apart to get you that fluid path or a 3D printed surround, or whatever you want to make, we'll give you that fluid path part.
And again, if you want to take that part and then do further validations, a more extensive study of the interior flow and pressure drop and other items that might happen that you would see in a more extensive fluid study, then you can take that part and do the same thing with that fluid path.
I want to show a few examples of what we mean by an efficient flow path. Go ahead. Next.
And so what we see is like three main areas. There's many, many applications that we have seen when talking to customers when we start talking about Generative fluid paths, but the three main ones that we see right off the bat-- they're very obvious to us-- are if you have complex ducting, and you need to avoid obstacles, just like Generative, right? You need to avoid things. You need to move air from one side to the other, or water from one side to the other, and you have a ton of things in a way, and want to know how can they do that without incurring a huge pressure drop. That's one area.
And as we were discussing earlier, if we wanted to have, in this case, a manifold being naturally balanced-- what I mean by that is if you were to take that top energy in the middle and run simulation, or just run a test on your flow bench, what you would see is that the two middle flow paths, the two metal pipes, they're going to get the majority of the flow. So what we're going to offer is the ability to say, well, we're going to give you an efficient flow path.
But also, we can naturally balance the thing. So they all get about the same flow rate without having to use valves in order to balance that flow out. Or if you wanted to bias towards the outside or inside, that ability as well. So that's what we're talking about when we want to have manifold distribution.
In the end, the most obvious use case is flow control devices. So you're moving-- in this case, it's a valve and a very simplistic design, just for illustration purposes on the top. And I'll go through a demonstration of this here in a minute. But the top one is like an original design. very simple. And the bottom would be the flow path that Generative Design for fluid paths would give you, which is not only a smaller volume, but a much lower pressure drop overall.
And to demonstrate that, I'll show you right now what we have in product. So Generative Design-- what you'll see is a new entry point, structural and fluids. And in the fluids, this is where you would need to turn it in to a streamlined interface that deals directly with fluids and the design requirements to run a general design of fluids.
You have all the same things-- preservatives, obstacles, starting shapes, obstacle offsets. They'll be very familiar to you if you use Generative structural components.
First thing we'll do-- same things. We'll enter in our preserves. So where our inlets and outlets are, we'll make those preserves. And so I have one inlet, one outlet in the south. I'll make those two preserves. And then I'll start identifying obstacles. So the connection points, the flange where the pipe would connect, I have the valve stem, the cage around the valve, the valve seat. I'm going to go ahead and make those all obstacles, because the flow has to avoid those, obviously. You don't want fluid volume where we have little parts that we're going to have as part of our assembly.
Now, if I were to make those bodies the only obstacles, what you would get is-- an efficient flow path would just go around the valve, straight to the outlet. So what I have here is an additional obstacle. In order to make it go up through the seat of the valve and through the cage, I'm going to go ahead and make an additional obstacle. So what I can do is just a simple extruded staircase part in this instance to make sure that the flow goes through the seat. It has no path but the one we want when we're trying to create a valve. So that's the obstacles.
Now, you can let the starting shape be automatically generated just like in Generative for structural components. Or create a really simple boxy shape. Or you can have your initial design, maybe your existing design. What I've done here is chose, again, like we had in the previous slide, a very simple initial design. Has the right size for the pipes leading out of the connection points, but a very simple globe around the actual device itself.
Last thing we need to do-- well, not the last thing. The next thing is to create a flow rate from the inlet. So in this case, I could use five gallons per minute, one hundred gallons per minute. I could see from my normal use case what the flow rate I want to design around is. And also, I have to have an outlet, so just zero pressure gauges is a typical outlet condition we use. That's very simple boundary conditions, or design conditions.
The next thing are part of the design criteria. Like we said, right now, we're creating efficient flow shapes, so minimizing pressure drop is going to be our number one priority. We're, much like in Generative for structural components, we're targeting a final volume, and this is a percentage of what our starting point is. So 25% will need really small, thin pipes trying to lead from point A to point B. In this case, we're going to knock it down to like 65%. So it's a pretty drastic reduction from the current volume.
Also, want to pick our study material. There's water, air, or any custom material. You just choose water in this case. And then we would Generate. So it's the same workflow, a little streamlined. You didn't see any manufacturing constraints. They don't exist in this is a preview feature, but will be adding features as we move forward.
So you would click on-- where you would select the solve in the cloud, this study or multiple studies at the same time. So if you have different flow conditions, or whatever you want, or different obstacle configurations, you can go ahead and study those all at the same time using the power of the cloud.
And here, when it's done, it's the same Explorer environment. So everything you have-- except it's tailored towards fluids-- so instead of safety factor, we're showing you pressure drop, velocities, things that are important for fluids.
My favorite is this graph. So we're going from right to left. Iteration one, reducing in volume. And you can see the pressure is still reducing all the way down to our target volume. So we end up with a very efficient flow path. So if we look at the pressure drop, you can see, obviously, the pressure high on the right. We can see that the flow is going the correct direction, and actually in a very streamlined manner, as opposed to a much more chaotic manner you would see from that initial design. It was not very efficient. You can see the bottleneck where it avoids the first obstacle in the very first design, for the first iteration, my initial starting shape, compared to the obvious bottleneck, which is where it should be going through the valve in this case.
And again, much like in Generative structure components, where exactly like that, you can create either a T-spline or a mesh geometry, and open that up and use it for further downstream uses. So in this case, it shows the actual physical geometry, that T-spline. And we're really happy with the shape quality right now. We're going to continue to evolve, make things better. But as you can see, a very symmetrical output on what is actually a pretty complex flow, automatically generated, and we're really happy to share this.
The cool thing is, because we are using Generative and all of the infrastructure that's for Generative structural components, the same downstream workflows. So you can see your obstacles in the recipe. You can edit the geometry directly as an after-treatment if you see something you want to change just slightly.
So we're happy to bring this to market. We're going to introduce more features. It'll be rapidly changing and evolving, and it is part of our Preview Features.
So the next slide, I'll talk about that here real quick. The other way. The other next slide, yeah.
So what we'll have is the fluid path study type. And again, pressure drop minimization is the main goal. The flow balancing-- it will be coming soon after you see this video if you're viewing this on time at AU. And we hope you're enjoying your AU, by the way. It is part of the Preview Features. It's where we deliver all our future mainstream, future sets and capabilities.
All the things that Mike pointed out that are not on by default are accessible here. So all our Experimental Solvers and features that he detailed out earlier in the presentation are all in here. And our traditional manufacturing methods introduced here first.
Our future capabilities for Generative Design for Modeling will also show up in Preview Features. So if you're interested in any of these things, go to your preferences, see what's in the Preview Features. Turn them on. Turn them off if you don't like them, or if you're not ready for them for your certain projects. But if you are really interested in these things, I really highly recommend turning on different experimental features and seeing how our product is evolving very rapidly.
All right, we will go the next. And as Mike also pointed out, m this can be very easy to get started. If you haven't already, you can activate your seven-day trial. You get a trial extension. You get a really vast array of sample files and tutorials to get you started. So it's never been easier than it is today to get started using Generative, and to explore and learn about all the new features and functionality that have been added over the last year.
And finally, I just want to say if you have more questions, we're always here to help. Just email us at GenerativeDesignHelp@Autodesk.com. We'll be glad to get back with you as quick as we can, and help you along your journey to a more efficient or automated way of designing parts.
And last but not least, like I already said we really hope you're enjoying Autodesk University. There are many, many more sessions available to you. I really invite you to look through the agenda and pick out classes. Even after Autodesk University is over, to go comb through the ones you weren't able to attend live, or to attend during the time frame of Autodesk University, and just learn the material. It's there, available for you, and we really appreciate your attendance.
MIKE SMELL: Thanks so much for that closing, Heath. As Heath said, I hope you're enjoying Autodesk University. We look forward to seeing you again at AU 2022. Thanks.