Thought Leadership

Talking Aerospace Today – Generative Design for Electrical Systems Part Two – Transcript

By Quinn Foster

In this episode of Talking Aerospace Today, Todd Tuthill and Tony Nicoli continue their conversation about generative design for electrical systems, and why companies should start investing in generative design now.

Patty Russo: Greetings and welcome to another episode of Talking Aerospace Today from Siemens Digital Industries Software. I’m Patty Russo and I’m responsible for aerospace and defense global marketing here at Siemens. Thank you for joining our conversation. The last time we met, we were joined by Tony Nicoli, who shared his expert insights on generative design. Welcome back, Tony.

Anthony Nicoli: Thanks, Patty. It’s great to be back again.

Patty Russo: And of course, a warm welcome to Todd Tuthill, our VP of Aerospace and Defense here at Siemens Digital Industries Software. Welcome again, Todd.

Todd Tuthill: Great to be back, Patty. Can’t wait to finish this really interesting talk on electrical systems.

Patty Russo: All right. Let’s just pick up where we left off. Todd, you talked more about what generative design means in the context of digital transformation maturity, and Tony talked about the impact and in some cases pretty significant impact that AI/ML can have on companies. Tony also talked about some of the solutions in our IES portfolio that help our customers realize these benefits. So let’s pick it up from there, Todd. What do users of AI/ML think about this new technology?

Todd Tuthill: Okay, so let’s look at that in a couple levels. First off, I want to quote a statistic that Tony used in previous episode. He talked about a one of our customers saving 94%. That’s nine four of the development time of some of their supportability publications with using this generative design software. When you have a number like that, you know the CFOs and the CTOS and the managers are saying, “Yeah, I want this now.” Right? They’re saying, “Give me some of that 94% savings.” So we have a lot of people very interested in AI/ML and generative design, and they want to get on the bandwagon. But then reality sets in. People say, “Okay, but how does this work and not just how does this work, but maybe how far can it go and how much can we trust it?”

Todd Tuthill: And I was reminded of a situation that happened in my life. I’ll date myself here. About 50 years ago when I was about seven to eight years old. So you can now do the math. That’s how old I am. And I go back to something I saw my father do and I was thinking about this as we were preparing the episode. It’d be hard probably for our younger listeners to imagine, but imagine a world where there were no electronics. There were no computers. There were no calculators. And that’s the world I lived in, you know, 50 years ago. And I remember the day, I remember the time when my father brought to our home our very first electronic calculator.

Todd Tuthill: I’d never seen one. He’d never seen one and he decided, okay, if whatever reason, it’s time. Right. And he brings the calculator home. And my dad, you know, I’m an engineer. My brother’s an engineer. My father wasn’t simply because he couldn’t afford to go to school, but I would always say, and it’s true, my dad was smarter than my brother and I put together. He was just a brilliant, brilliant person in all the things that he did, and he brings his calculator home. And my dad was brilliant technically, but he was not real trusting of new things, and he brings the calculator home and before he would use it in any kind of real work, he had to prove to himself that calculator could do arithmetic at least as good or better than he could.

Todd Tuthill: And you know, this was just a four function calculator. Multiply. You know, divide, add, subtract. That’s all it did, but I remember sitting there watching him, doing all this math. He did it manually and that all the math on a calculator before he convinced himself that the calculator was ready to use. And, and I think we’re seeing that now and are going to see that with these generative algorithms because they almost sound too good to be true. Like the first time you held the calculator or the first time you held a computer. Can a computer do this? Can an algorithm do this?

Todd Tuthill: And the answer is yes, it can, and we want to obviously take our time. We’re creating aircraft here that people are going to fly on. We want to know they’re going to work, but I think that’s one of the barriers and one of the questions that a lot of people are answering with respect to generative design. Can we trust it? And how far can we trust it? And how much verification of it do we need to do? And is that any different than the verification we do with the artifacts created manually?

Patty Russo: I’m kind of like rendered a little bit speechless on that because a) I’m like taking myself back in years to kind of similar experiences. But yeah, Tony. What’s your thought on what customers are saying about AI/ML and/or the idea of trust?

Anthony Nicoli: Before we get into the details there, I really echo with what Todd said because my dad, my uncles were all measure twice, cut once kind of guys, so I can relate to what you said, Todd. And here’s the thing. The development process we’re talking about EE systems and electrical today. It’s, and it’s actually not any different in any discipline, It’s complicated. Doing A&D stuff, we don’t go and make platforms and systems that are simple, right? Aerospace and defense deals with some of the most difficult missions that the human species faces.

Anthony Nicoli: And like Todd said, we want to be safe. We want to execute on them. So you got to trust this stuff, and part of the whole idea of augmenting people with the generative algorithms is that you get to have the people in the loop to make sure that as we go through this transition from traditional interactive approaches to more fully machine-based approaches, we believe that the algorithms are going to keep us safe and so there will be a series of years, I believe, where people are going to be watching over those algorithms. And then as things move along, they’ll increasingly trust them, and then they’ll let go.

Patty Russo: No doubt the other topic that resonates is, taking the calculator example, Todd, I would imagine that when once your father realized that he could trust that calculator, the amount of time savings that he realized not doing his equations manually saved him quite a bit. The same idea is true with what we’re talking about. And speaking of time and age and that sort of thing, there is a research report that shows that over 29% of the industry’s workforce is over the age of 55. So there’s definitely an impact on retirement that we’re going to see in the next few years. I know we’ve talked about this, you know, in at least 10 to 15 years that I’ve been around this industry, but even more significantly now. So where do you see this impacting the adoption of generative tools and this type of technology that we’re talking about?

Todd Tuthill: Yeah, that’s a pretty sobering statistic, Patty and another statistic that I’ve seen recently is that right now one in ten engineering jobs in aerospace are unfilled, and by 2030 it’s not going to get better. It’s going to get worse. It’s going to be one in five. So what that says to me is that for industry to continue to thrive, Tony talked about all these incredibly great things, these complex things we create, and that’s in aircraft, that’s in space. I mean, think about all the things that are in space, going back to the moon, going back to Mars, and then think about the sustainability challenges that we have as a species and that we’re facing specifically in aerospace and how to take carbon out of the propulsion that we use in our commercial aircraft.

Todd Tuthill: There’s so many exciting, interesting but important challenges that we have to overcome and it’s getting harder and harder because the number of engineers to do that relative to the need is going down and down. So we can’t afford to have engineers doing those mundane things any more than my father could afford to do all this math with a pen and with a pencil and a piece of paper. He had bigger things to do. He needed to have his productivity go up by having a tool he can trust, and that’s what we want these things to be a tool we can trust that multiplies their impact, just like the calculator eventually multiplied the impact of my father’s work, just like as you know, the generation Tony and I came up with. It wasn’t calculators that it was computers.

Todd Tuthill: They came in as we were in college that we started to use them, and that multiplied our impact. And now this generations multiplier is going to be AI/ML, and generative design and those things, and they become absolutely essential, I think for us as a society, as an industry to continue to do all the great things we want to do because we want the engineers to be freed up to do the critical thinking and the critical decisions and not the mundane things. Typical wiring design is going to become a mundane thing that human beings just don’t need to do anymore.

Anthony Nicoli: So I want to get in here on the time topic because I think it at the end of the day, it really is all about time and accelerating what we do. When I think about like the sustainability challenge that we have in front of us, the only way we’re going to get there is by iterating things and the iterations need to go a lot faster. And part of that is doing them virtually, but part of that is extending, you know, generative designs capabilities today with AI/ML.

Anthony Nicoli: Most of what we do today is rule based, but the potential for AI/ML really takes us to a different level. To me one of the biggest areas where time gets consumed is when we have to deal with changes, when we discover that we have to deal with changes late in the game, and right now we have the ability to do integrated checking to basically apply heuristic knowledge and explicit knowledge to making sure that we make fewer mistakes as we do our work and that’s having dramatic impact.

Anthony Nicoli: I mean, there’s L3 Harris, for example, and their flight simulators took rule based techniques and in the eliminated 90% of their change orders the first time out, right? But when we go to AI/ML, I think we’re going to be doing a whole different level of elimination of error and also of accelerating iteration.

Todd Tuthill: Yeah, absolutely. And you talked about one of the key topics that are so necessary for this phase of digital transformation, and it’s faster iterations. Which leads to something I like to talk about, which is optimization at a whole other level, because I think about a lot of the engineering projects I’ve been involved with. There comes a time where there’s a critical design review and you got to put pencils down and the engineers have to stop doing designs, and we got to go build something. And the reality is, when you’re using, I’ll say existing kinds of more manual design techniques, there’s a finite number, usually a pretty small number of design iterations you can go through for something as complex as a spacecraft or an aircraft.

Todd Tuthill: You can only do a few because there’s just the teams, maybe a hundreds or thousands of people that have to be coordinated and all this stuff is manual and you know, if I tweak something over here, it tweaks something over there and, you know, and I imagine most of our listeners have been through some of those reviews. There’s a small number you can do, so what you what typically happens is each of the subsystems maybe get optimized in and of themselves, but the overall vehicle, the end product tends to be sub optimized because a lot of the margin is held in the subsystems.

Todd Tuthill: Well, these new techniques with AI/ML and optimization, and we’ll get into optimization in a later podcast, but that’s a key part of this too, but when you can do generative design, it leads to optimization. You can do many, many, many iterations and that’s really what we’re talking about. The kind of the next generation of opening up the return on investment, allowing us to do hundreds of thousands of iterations and really optimizing around those and optimizing at the higher level. And there’s just so many exciting things in store for where this technology can take us.

Anthony Nicoli: So one of the really cool things I see in that Todd is how advanced algorithms can direct us into where change impacts the overall development of a platform in a multidisciplinary way. Today somebody makes a change and then you almost have to call people on the telephone and let them know what’s going on and you have to mediate that. We have decent ways of doing that in workflows and stuff, but to get to the point where we can automate an algorithm can identify the tentacles of impact and then just coordinate the team the team to converge on a decision right quickly, that’s going to remove a ton of temporal obstacles in these iterations.

Todd Tuthill: So let me get this straight, Tony. You mean that I won’t have to use Excel anymore to make list of things I need to change and remember? That these algorithms will do it for me?

Anthony Nicoli: Oh my God, you’re killing me, dude.

Todd Tuthill: What a wonderful day. I’ve been looking for that day my whole career.

Anthony Nicoli: Yeah and we already, you know, we already have change propagation technology that points to this, but I think we’re on the verge of a completely new level of what’s possible.

Todd Tuthill: Yeah, I’ll make a prediction. And I made this prediction last episode, but it’s appropriate here too, that when we talk about AI/ML, and the adoption of AI/ML, I think it will change the way aerospace products should developed substantially in the next ten years. And I think ten years from today, there will be two kinds of aerospace companies. There’ll be aerospace companies that have fully adopted and bought into AI/ML and there’ll be companies that are no longer in business. And I really think this is a transformational moment in aerospace.

Patty Russo: No doubt, especially as you talk about things like the need to accelerate iteration, the number of engineers that are needed in the industry, the idea of a new level of optimization. So thinking about those topics and adding in the idea of increased competition with new technologies, new types of aircraft coming online, companies that are competing at a higher level, our customers want ROI now. They want to do something now that’s going to have significant impact. So Tony, I’ll start with you with this next question. What do you suggest that customers do today?

Anthony Nicoli: I think they should adopt the existing generative capabilities immediately. I think that they shouldn’t wait. They can get a lot of improvements by applying general design in conjunction with a coherent model based digital twin and a digital data continuity throughout the digital thread. They can do it right now, and here’s the thing, too. They’re going to get on a train that is improving every year. We have a very rich future roadmap and as I said in the last podcast, I think this is very much a young technology, and as we start to see the AI/ML roadmap come to fruition, it will integrate into the development infrastructure that they invest in.

Patty Russo: So, Todd, where are we going in the future? How do I get from point A to point B? Where do I go today, especially in light of the idea, Tony, you brought up where change impacts in a multidisciplinary way. How do we get from where we are today to the kind of generative design Todd that you talked about earlier?

Todd Tuthill: Yeah, I think that’s really at the heart of digital transformation maturity, the heart of what we’re talking about when we talk about the Siemens Xcelerator and our suite of solutions. We talked at the top of the first episode about the building we’re building and the foundation of configuration, of connection, of automation. And those are critical aspects that really allow a company as they mature their digital transformation to get to a point where they can first do generative design in a case like Tony’s talking about, in one domain, an electrical systems. Once you’ve done it in one domain, then you can add a second domain, maybe mechanical systems, and you really get to that whole idea of optimizing around a product like we’re talking about. And that really requires that integrated multidomain generative design and integrated multidomain optimization, which is where we’re going to go next in the next episodes of this podcast.

Todd Tuthill: Tony’s done a fabulous job today, painting a picture of where we are today in electrical design. We’ll have another guest from mechanical design on the next episode of the podcast talking about the mechanical side, and, you know, the tying it together through accelerator in a multi domain optimized approach. It’s probably overwhelming. I’m trying to imagine the person setting there who’s saying, “Okay, I’m just trying to get the people in my shop to stop using PDFs, you know, and use a 3D model.” And there’s a lot of people out there like that, and you’re not going to get to generative design overnight.

Todd Tuthill: It’s a maturity process. We like to say digital transformation is a journey and that’s why we talk about this maturity matrix of five steps. Every company is in one of those five steps right now in their journey and it’s not important to get to step four, you know, or step five next month. It’s important to assess where you’re at and think about what’s the next step to go further. It’s about the journey. It’s not about getting to a destination in the next month.

Patty Russo: Yeah, and Tony, you mentioned the idea that “don’t wait.” Is there anything else you would want to leave our audience with?

Anthony Nicoli: So in the 90s, Norm Augustine, a little tongue in cheek, predicted that at some point the next aircraft program was going to, like, consume the entire GDP of the United States. Okay? Generative design is one of the things that is making that untrue. That’s like the end of aerospace and defense development if everything costs that much and takes that long. So is Todd said, don’t wait. Get on this wagon because you’re either going to be there or you’re going to be out of business.

Patty Russo: Thank you to Todd and Tony for your incredible insights, and thanks to all of our listeners for joining us. We really hope that this conversation and our other episodes around these topics help you learn more about what generative design is and how it can help you on your digital transformation journey. Next episode we’ll focus the conversation around generative design in mechanical systems. I’m Patty Russo. We’ll look forward to having you join us on our next episode of Talking Aerospace Today.

Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.

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