0:02 All right, everybody. It’s pretty awesome. Hasn’t this been really awesome? The different things that we’re seeing and hearing here today. I mean, like, you know, what we just heard throughout everything that I’ve seen today. And I I I’m going to have some slides. I may talk about what’s on the slides. I kind of just want to talk through different things more, you know, just kind of a story.

0:24 But as we look at like the advancement that we’re seeing here today, it is phenomenal. If I think back to let’s say 20 years ago actually let me ask a question. How many of you know what Forran is? How many of you have coded in forran? Few. All right. Yeah. And we’ve got you know a few of the the people here. So when I look at the difference of what simulation and what some of these capabilities are relative today compared to what I was doing 20 years ago was coding Nastran input decks in forran or you know I was using fluent and then this company called ancis bought them I was like what’s going on now I work for ances things are crazy if I even look at back to say 10 years ago I’m a research portfolio manager at Boeing doing additive manufacturing, working on new machine development, working on materials, working on applications.

1:25 There was no process simulation available for me. I’m sitting in the middle of a lab in the middle of the night sitting there running process parameter optimization and thinking why isn’t a computer doing this for me? So look at that. So that’s like 10 years ago, you know, 20 years ago is 4R. 10 years ago, there were a lot of capabilities. We’re missing some things in additive.

1:48 I look at what’s available today and what we’ve seen from all of this these past conversations and the the presentations. It’s absolutely amazing and I love it. And so that’s why I’m happy to be here today. Like I’m talking about computational design for design optimization. So I don’t have to I just get to talk about cool things that I think are interesting. I don’t have to worry too much about going into details.

2:13 So this slide says sustainability. My motivation in you know what the reason I got involved and why I grasped on to additive manufacturing over 20 years ago was not just sustainability but because of the fact that digital engineering things like additive manufacturing can help to improve the quality of life. Across the globe in our generation and others. Sustainability is definitely a piece of that. As we look at being able to truly optimize designs from forward-looking simulation as well as other techniques, then we actually are able to proceed or progress the progress with being able to improve the quality of life.

So, I’m going to let I’m actually just going to kind of jump through a few of these. This slide we’re talking about just things where mistakes that could have been avoided with simulation, how much money that costs and and if there’s something that could have been avoided with simulation or with other type of capabilities that is not solved, who benefits from that? Not very many people. You know, if you look at back in this in the 70s, an automotive company released a car with a bad fuel tank.

3:38 It was an $11 change that they chose not to make in order to fix that fuel tank issue. That ended up costing them more than 10 million times that much money in liability and repairs. Not to mention the more impactful thing that there were people who lost family members and friends to deadly fires from that. All could have been solved. You know today and that obviously in in the 70s the computational capabilities weren’t the same back then but today you run a quick sheet metal stamping simulation you know exactly what’s going on with that fuel tank and you don’t have to run through some of those problems.

4:18 We can also see you know these examples up here. There’s a lot of money wasted as Oh, more microphone. Thanks. I was wondering what you know what you’re pointing at. No, sorry. All right, I’ll try and keep the microphone here. There just so there’s so much waste and that’s the thing that I want to get rid of is getting rid of waste. Now, Bradley talked a lot about being able to drive design iterations further forward.

4:49 So, I, you know, I talk about moving it further to the left in the design cycle. Let’s move it all over there. But I don’t know if any of anybody else has ever gone through the same thought process as I have, but I’ve thought, well, wait, if we move everything over to the left, doesn’t the left just get bigger? And you’re like, wait a minute. So, so what’s going to happen?

5:10 But the reality is, yes, the left does get bigger, but you completely eliminate a lot of what’s happening on the right. And you eliminate so much waste and so much unusable iteration. And so just like Bradley was talking about, we we don’t have to be fearful of failing if we’re failing far to the left of the product development cycle because that’s the place where failure and learning is quick and we can actually benefit from it before we get locked in.

5:43 So as we as we look at that I want to just go through and I just want to share some examples of some different types of of physics based analyses that can drive computational design. So, this company the company well yeah the company that made this product Optus some of you may have seen just recently they had one of their first RF additively manufactured components get flown into space I think last week maybe an extremely innovative company they’re awesome they have patented capabilities to be able to go and they’re building RF devices well they found a way to be able to optimize those devices and print them with additive.

6:28 So if we look at this particular example this thing weighs onetenth of what even just regular Sband components this is a tri a triband component even just an Sband component that would do the same thing usually weighs about 10 times as much as this. I love the quote here from one of their customers. Wait is somebody holding that with one hand? You know these are big things and they do that by being able to use forward-looking simulation capabilities.

So they I’m yeah I mean they go through the process of being able to use simulation. I’m going to I I put this one up here because I like some of these the pictures just kind of look cool. So this is, you know, I don’t need to talk, this isn’t that same device, different things like that. But it does, it does bring up some of the capabilities.

7:19 We can go and we can analyze the RF performance of multiple bands of signal all simultaneously. We can drop it into a meta model and optimize based on all three of those bands. You can also take that to the next level and consider manufacturing with that. In their case, they’re using additive manufacturing. You can put those constraints into into the metal model and be able to come up with a truly optimized design.

7:50 And then we can also be able to avoid some of the waste that happens often times. Anybody that’s involved with metal additive manufacturing, you know that there are times where you run into issues. You may have a recodater interference issue. You may end up getting distortion in your part. You may end up with other problems. And we can analyze all of that clear on the left side of the timeline.

8:18 So you’re doing the RF analysis, you’re doing the structural analysis, you’re doing the manufacturing analysis, tying all of that together to make truly optimized components. I think it’s pretty awesome. You know, everybody has seen a lot of topology optimization. And I don’t want to spend a lot of time here because we’ve we’ve talked a lot in this space. But the thing that is really awesome with topology optimization tools today is you can actually take a topology optimization and add in manufacturing constraints.

8:51 And so you know you can start to go through a true optimization with with that back to CAD capabilities. All sorts of opportunities there. I’m not going to spend a lot of time on that one. But it’s kind of a little bit of a leadin to this next example. So, not only do we have the opportunity to do things like RF analysis, with manufacturing analyses, this is an example of being able to take a tradition more traditional manufacturing sheet metal stamping.

9:20 It’s been around for a long time. We can take that, we can integrate it with other analysis tools and and be able to truly make some some opportunities to improve the the workflow of different capabil of different different software tools. I was I was talking with a a German auto OEM company this earlier this week and they were talking about how they actually end up driving the designs of their cars.

9:56 The optimization for that is often times offset by the cost of having to retool their machine, their factories, right? And so in this particular example, we’re going to look at using topology optimization to reduce the weight of a die for a stamping simulation. In that scenario, there are times where they may be able to make a larger stamping a larger stamp tool, which a lot of times they can’t do because of the die weight.

10:24 And so a lot of time sometimes it may be geometric and other constraints, but in those scenarios where the die weight matters, you can actually go through this process. So you know in this particular situation we’re going to figure out the me maximum pressure on the dies. You’re going to go through and do a topology optimization to minimize the mass. Go back to CAD in that and then go through the manufacturing process simulation to make sure that you’re getting everything right.

10:51 And these are just some some videos showing some of the sheet metal stamping. We go through that process. We figure out the pressure on the blank that you’re forming which then we can apply that to the two different dyes and that is the loading condition for a topology optimization. So then we take that you can apply that you do the topology optimization generate a 60% lighter die.

11:15 Now, this is on a pretty small die in particular, but reducing 60% of the weight. Now, you’ve just expanded the size of component that you can make on the same size of die. And then you can also run this through the again, you know, this die would then need to be manufactured with additive in this particular case. Which you can do and often times especially in type scenarios that ends up being a a good local sourcing option especially for for when you’re going through the prototyping cycle.

11:49 So the next example that I’m going to talk about is looking at acoustics which we heard about acoustics analysis and again with sheet metal sheet metal analysis. I’ll go through some of this fairly quickly but this is an example of being able to take a sheet metal part and doing a topography optimization. So be and basing that topography optimization off of the acoustic performance the stiffness of this sheet metal stamped part.

12:25 So we can go through that you can generate a bunch of results. We put it into a meta model and you can go through the iterations using AI capabilities to be able to look at all of those different scenarios that you can use in that meta model to find a truly optimized design which then we can take that and go over and go and run a final acoustic check where we have modified the topography.

12:55 So in this particular case then we put we put curvature into the panel that then we stamp into the panel to give it exactly the acoustic response that we want. And then you can also utilize that again forward-looking simulation. We’re bringing the manufacturing simulation as well. Is that going you know are you going to get too much stretch? Are you going to get stretch marks? Zebra stripes things like that in the sheet metal stamping process.

13:22 All of this is happening in that very beginning side of the of the timeline which again this is helping us to be able to truly drive improvements in the world for for us and future generations. So just as kind of an example on the timelines of things that it takes to do this setting up some of this stuff. I mean we’re talking this is these are numbers for an experienced user.

So, if you haven’t ever used some of these tools, it’s not going to be quite that fast, but we’re talking about several minutes of setup time. And and in the simulation side of things, there are times whether you’re using a GPU solver, a CPU, depending on the the solution that you’re doing, it may take some time to run the simulations. But the scenario is you can create a meta model from spending that upfront time that then it doesn’t ma you know essentially we’ve got 2,000 design points in this particular case that we can evaluate as needed and and be able to use.

14:25 So there’s this is just stepping through kind of some of the things we do in FE analysis structural optimization validate it and take it back to CAD to be able to modify the the dice surfaces. So just a quick video showing some of the acoustic and performance analyses that we’re seeing here. I’m going to go through most of this fairly quickly. Just an example of kind of some of the the meta model capabilities that we have here.

14:57 And I just want to touch on just as we move on to some of these other capabilities, as we look at the opportunity to be able to leverage multifysics simulations into improving things. I’m just going to touch on a few other places. I’m not going to go in depth on some of these others. Heat exchangers. I think most of us have seen a lot of examples of doing heat exchangers.

15:21 It’s an awesome place and I and you know it’s like one of those things where we’ve all seen this. We’ve all seen these things. There are a lot of opportunities though where we can gain a lot of value from optimizing heat exchangers through doing thermal structural and manufacturing process simulation up front. So then we move into other opportunities where we can, you know, if you’re looking at a at a ball grade array of solder for for the high-tech industry, you’re looking at solder reflow.

15:54 You can go in and you can upfront be able to look at how the how the solder is going to flow to be able to understand how you need to place your your BGA for you know and how you need to distribute that very early on in the development cycle and not only for the solder reflow but you can also do things like looking at the fatigue analysis to make sure that you’re truly optimizing the process early on.

16:22 Also, you know, if you’re looking at silicon manufacturing and get into optimizing the fluid flow from the shower head from that from that manufacturing process. Lots and lots of other examples that I could throw out here. We know that there is a lot of opportunity to leverage digital engineering to improve the overall capability and optimization of designs. But the thing that we you know collectively everybody working together that’s you know I I just love the opportunity where everybody’s here sharing.

16:58 It’s a culture of driving success and it takes a mind shift a mindset shift in order to get there. You know we have to change we have to have a a change management mindset and go through the application of digital technologies. And you know, it depends on how we do things, how we work, and how we develop. As we change the way that we’re doing those things collectively as an industry and as a group, then we see very very big benefits for everybody and it improves the life for all of us as well as other people.

17:34 And that’s all that I have to share. To learn more about the CDFAM computational design symposium series, to see the archives of previous presentations, and to learn about future events, visit CDFAM.com.