The Additive Advantage Podcast
In today’s volatile markets, organizations face a brutal balancing act: the relentless pressure to innovate faster while maintaining operational excellence. Additive manufacturing (AM) was supposed to be the game-changer. But for many companies, it’s become a slow burn of money, time, and credibility.
We’ve seen it up close: $4 million spent, 18 months passed, a dozen engineers assigned—and still no outcomes. Pilots stall. Production doesn’t scale. ROI never makes it to the P&L. If you’re a GM or SVP who championed AM and now find yourself watching money burn while results slip away—you’re not alone.
The truth? Most companies treat additive as a technical side project, handed to engineering and isolated from the business, with the expectation it will somehow deliver like magic. But innovation without execution is just expense.
That’s where the Additive Advantage Model comes in—and this podcast brings it to life.
Hosted by Shon Anderson and Dani Mason, with a combined 20 years of additive manufacturing experience, The Additive Advantage Podcast brings you real conversations with industry leaders who have been in the trenches of transformation. These aren’t fluffy tech chats—they’re straight-talk interviews about what it really takes to make additive deliver.
The Additive Advantage Podcast
EP 17: Why Predictability Is the Missing Piece of Additive Manufacturing
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A failed metal build doesn't just waste material—it can waste days of machine time, thousands of dollars, and delay an entire production schedule. So why are so many manufacturers still relying on trial and error?
Christian Rossmann, Ph.D., Founder and CEO of NODiVEC, joins us to explore why simulation is becoming one of the most important tools in modern additive manufacturing. Drawing on more than 15 years of experience in metal additive simulation, Christian explains how manufacturers can identify distortion, cracking, and other build failures before ever pressing "Print"—reducing scrap, improving quality, and building confidence as additive moves into production.
The conversation also explores why simulation has historically been limited to large organizations, how AI is helping democratize engineering expertise through a "co-engineer" approach, and why the future of additive manufacturing depends on smarter workflows—not just better hardware.
Whether you're running a service bureau, scaling production, or evaluating where additive fits in your business, this episode offers a practical look at how predictability becomes a competitive advantage.
About the Show
The Additive Advantage Podcast explores what it really takes to turn additive manufacturing into a scalable, performance-driven business capability. Hosted by Dani Mason and Shon Anderson, the show features real conversations with leaders accountable for outcomes — not hype.
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About the Hosts
Hosted by Dani Mason and Shon Anderson, industry leaders with deep experience in technology and additive manufacturing.
When you do this at home with your plastic printer, not a big deal. It takes a couple of minutes, you know, to reset the printer and to start printing the process. But nowadays, when we look at metal, industrial metal additive manufacturing printers that print very large parts, some to you know, up to about a cubic meter of build volume, where they print rocket parts or very large parts for the defense industry. If a failure occurs when you print such a large part, it's a huge waste because chances are very, very slim that you can restart this printing process and continue when this failure occurred. So, more likely, what you have to do is you have to take everything out. It's basically considered scrap, and you have wasted time and money to try to build a part. Now, this is where simulation comes in.
SPEAKER_02Welcome to the Additive Advantage Podcast. I'm Sean Anderson. This is where we explore people, technologies, and strategies that are shaping the future of advanced manufacturing. My co-host Danny Mason and I. Yeah, I'm sorry, I don't know what. Clearly it's not 9 30 at night. Yeah. I was reading two paragraphs at once and just kind of randomly mixing them together. That's how nope, this is all good. That is how my brain works all the time. Welcome to the Additive Advantage Podcast, where we explore the people, technologies, and strategies that are shaping the future of advanced manufacturing. I'm Sean Anderson, here with my co-host Danny Mason. Whether you are leading an additive program, evaluating technologies, or just looking for practical ways to improve manufacturing outcomes, we're glad you're joining us.
SPEAKER_01Today's conversation tackles a topic that often flies under the radar in additive manufacturing, but arguably has one of the biggest impacts on production success, simulation. If you've ever had a metal print fail after hours or days of build time, you know how expensive trial and error can become. But what if you could identify those failures before ever pressing print? Joining us today is Christian Rossman, founder and CEO of Notavek. Christian has spent more than a decade at the intersection of metal additive, simulation, and predictive engineering, including time at Materialize and founding Additive Lab before launching Notavek. In this episode, we explore how simulation is evolving from a tool reserved for large companies into something accessible for manufacturers of every size, and how AI may be the catalyst that finally brings simulation into the mainstream. Let's dive in.
SPEAKER_00All right, so my name is Christian Rossmann. I was born in Austria. This is where I grew up. Um and I followed an academic career for quite some time. So I studied mechanical engineering, um, did a master's in business engineering, and um got my PhD in computer science. And um that PhD was partially completed in um at the medical university here in South Carolina. So this was the first time I actually moved to the US and uh got to experience what life in the US is like. Then in 2014, my wife and I we moved to Belgium, and when we did that, I was actually looking for a job. So traditionally, my experience was in the traditional mechanical engineering field. But in 2014, when we moved back to Europe, I got a job at this company Materialize, which I'm pretty sure most of your audience uh know this company. And there I um got exposed to metal additive manufacturing, more specifically, how you can use numerical tools, predictive tools to um investigate what's going on in the printing process. And I'm assuming we will talk about this a little later. So there's a lot of things that can happen that result in failures which cost companies money. Um and so yeah, I got exposure to that, and we started investigating different processes um uh and different methods and how to do these predictions. And then in 2017, 2018 is where I found my first company, Additive Lab, um, in Belgium, that um was basically a research tool with um a variety of um numerical tools and applications to do to answer questions that one may have when it comes up to the metal additive manufacturing process. And in 2022, I my wife and I and my my family with our child, we moved back to the US. And this year, with having you know more than 15 years of experience in the additive manufacturing simulation field, specifically on the process prediction side. Um, I felt a need there's I felt there's a market need for a new tool with um a lower a lower cost burden that allows users with lesser budget to utilize the advantages of simulation and the predictive power to optimize and improve their metal additive manufacturing processes.
SPEAKER_01It's wonderful. What a what a robust history too in the additive field. And you've touched so many different facets of it. I think what would be helpful, um, because our audience, some people are familiar, you know, with why you would do pre-print analysis or simulation if they have done metal additive. A lot of folks maybe have less familiarity. For someone who is unfamiliar with this process, what types of things can a simulation tell them before a part is ever printed?
SPEAKER_00Yeah, so to me, I'm very passionate about it. I'm gonna be very honest with you. I do not understand quite why not everybody with a metal 3D printer is using predictive simulation software. So when you have a metal additive manufacturing process like laser powder bit fusion, direct energy deposition, um sinter-based processes like metal binder jetting or cold metal fusion, all these processes, there's a lot of complicated physics happening that can lead to manufacturing failures. So everybody, even with a with a desktop 3 printer at home, I'm sure has experience you start the printing job and then all of a sudden the job fails. So you have to redo it all over again. When you do this at home with your plastic printer, not a big deal. It takes a couple of minutes, you know, to reset the printer and to start printing the process. But nowadays, when we look at metal, industrial metal additive manufacturing printers that print very large parts, some to you know, up to about a cubic meter of build volume where they print rocket parts or very large parts for the defense industry. If a failure occurs when you print such a large part, it's a huge waste because chances are very, very slim that you can restart this printing process and continue when this failure occurred. So, more likely, what you have to do is you have to take everything out. It's basically considered scrap, and you have wasted time and money to try to build a part. Now, this is where simulation comes in. So, what simulation does is typically in form as a uh of a software tool that takes in, you know, for the most part, a couple of minutes, maybe up to an hour of time, that you load into uh your geometry. So when you prepare your build, typically some 3D geometries, you run the simulation and it will tell you if there are certain problems. So typically that's presented to you in the form of a heat map that is projected on the geometry, and there's some coloring, typically some red spots that indicate the way you currently designed your build and you're trying to send it to the machine. This will lead to a failure. And so that's the true power of the simulation. It comes in before you actually start a printing process, and right after you have an initial idea of how your build preparation, your build job looks like. This is when you run a simulation, you get feedback, it will tell you, yes, this is good, good, you get a check, and then you send it to printer, or you will see, hey, there's a couple of red spots. You need to address those by changing the design, changing the orientation, changing the support structure, changing machine process parameters, and then redo the simulation until you have a green check mark and send it to the printer. And this will truly allow you to reduce your scrap, your failure rate. And the math is fairly easy because when you have a large printer, and let's say you know, you nowadays average build volumes for specialized materials or even standard materials are starting, you know, 250 millimeters cubic. So that's on the smaller end, and everything above is just bigger. And when you have a print that fails on that scale, that's immediately a couple, if not several thousand dollars that you lost and you cannot recoup. So you have to redo it all over again. And if you invest in a software tool, this scrap rate goes down. And it's just it's easy math. If you have a facility and you know I have this many failures a year, I can invest in software, do this prediction, optimize my processes, and considerably, if not significantly, reduce your failure. So that's the whole idea of simulation, and this is where it comes in.
SPEAKER_01That's great. And I I love that idea of ROI and putting the manufacturing and additive manufacturing when you think about reduction of scrap rate. I'm curious, you know, you mentioned some variables, design, support structure. With the companies you work with, what are the most common failure modes you see that companies should be aware of as they're building this case in terms of why they might need simulation software?
SPEAKER_00Yeah, I think this is a great question. Um the most common issue that I see in projects, and I've processed personally over hundreds of um customer projects, but the most common issue that I see is distortion warping that occurred due to the thermal mechanical nature of the manufacturing process, so unwanted distortion, as a matter of fact, or some sort of rupture tracking that occurs during the process or after the process. And both potentially, if not very likely, result in rendering the part not suitable for service anymore. So, for example, when you know you build a rocket part, a rocket nozzle, or some topology optimized structure for lightweight applications, a lot of materials used in metal additive manufacturing will experience some type of distortion. And that's mostly due to the subsequent heating and cooling that occurs in each and every layer. Um, and it expands and contracts, and that just leads as a consequence to distortions. And the larger the machines become and the larger parts users print, these distortions become more dominant and of bigger consideration. And so the most common failure mode is really between those two, where you see people, hey, I try to print this, but it's distorted it and I cannot use it for my application anymore. What do I do? Same question with um, hey, uh this started to delaminate from my bill plate or started to delaminate from um like where the part delaminated from the support structure and then resulted in a um build machine crash where the recoder started to hit the structure and the machine just stopped. Those are the two most common ones that I see. There are for very, very high-end application, and we're talking now very high-tech aerospace and defense industry applications. There are concerns regarding material properties and microstructure resulting due to the printing process. So this becomes of importance if you have high-end application with a lot of dynamic load exposure. The material needs to withstand um a lot of alternating loads or or high impact loads, sometimes under extremely high temperatures. And when you have an environment that is so harsh and where your printed part needs to be able to withstand all these types of loads, microstructure becomes of importance because it essentially defines how your part will respond to external loading. And so for very high-end application, it's not uncommon that users look into what microstructure results as a consequence of our printing process. So, to give you a more tangible example, when you print a large rocket nozzle, you start printing, and due to the fact in laser powder bed fusion, for example, uh, when you keep printing, the fur the further up you are in your printing process, temperature will start to accumulate in the sections that are already printed. And it's just because you keep putting heat with the laser, uh you keep putting in heat from the top, and that heat starts to accumulate, which as a consequence changes on how your melt pool, your melting operation uh per layer occurs. And to be very practical about it, or in a very simple explanation, the higher your baseline temperatures are with the same laser um parameters, your melt pull tends to become larger. That means the larger melt pool changes how the material starts to um cool down locally, and that as a consequence changes the microstructure. And this is very, very important to know, especially when you want to ensure that you need fatigue, lifetime capability of a jet engine part of a component, and that's just very important that you can investigate that. So, long story short, there are the more common problems of distortions and ruptures, and then there are some very um dedicated resource questions where simulation comes in and can help you answer those questions.
SPEAKER_01I think that's such an important point, this idea of a reactive versus proactive additive workflow, because to your point, especially in those high-end specialized applications, the further downstream you get, the more expensive failure is.
SPEAKER_00I mean yes, absolutely.
SPEAKER_01Free print, great. Post print, painful. In the field, catastrophic. So I really like uh the way you're walking people through how this is not only an ROI on the printing portion, but it's really a huge enabler of success in the field and a good customer experience and safety. Um, I want to also talk a little bit about expertise. You know, obviously you have uh a very robust background, you've done hundreds of these yourself, you have a lot of education and experience around this, and historically, simulation has required that kind of highly specialized expertise. Help maybe the our listeners understand why has interpreting results been, I'm gonna use the word difficult, but maybe a better use of the term is required a level of expertise that someone like you has that is not widely accessible.
SPEAKER_00Um yeah, that's a very good question. Um historically, when we look at how simulation, and I'm talking specifically about um finite element-based simulation, that has been typically carried out by experts that you know have gone some graduate studies through through some graduate studies and um have had years of training on how to prepare simulation models and how to interpret the results. Not specifically for additive manufacturing, but just generally speaking, the traditional career path of a simulation engineer, whether they're looking into traditional mechanical stress analyses or uh lifetime or durability predictions based on finite element results, that traditionally is carried out and or has been carried out by engineers with exhaustive training and a lot of hands-on experience. Now, when the first simulation tools, predictive simulation tools, started to appear in additive manufacturing, more specifically in metal additive manufacturing, and we're talking about um, I think the early really commercial tools were available anywhere from 2012, where you had the first solutions, till you know, really 2015 till 2018, when more and more tools started to boom and come out. But the first ones, uh the first tools based on were developed based on general finite element applications on finite element tools that were on the market. And so traditionally they had inherited the workflow, how finite element engineers traditionally used to work with these kind of tools, meaning a lot of, you know, you you need to know how to define boundary conditions, how you define material models, how you define details about running the simulation. So rather complicated a process. But then again, in 2015, around 2015, tools started to pop in, pop up that made this change a little bit easier or made prepare preparation and execution of these uh predictive runs simpler. And that I think started to democratize the simulation tools a little bit in terms of how do I uh start from a 3D geometry that I want to send to the printer to actually producing results. So that step became more democratized. But then on the latter end, as now you have simulation results, the big question, the big question is what do you do with it? Um and there, and up to this day, I think it's still a bit of a challenge because when the simulation tells you, for example, that there will be a distortion, what do you do from here? The first thing, and I'm speaking of experience with customer projects that I have been working on, is you need to understand where does this distortion, does this placement come from? Is this a buckling related issue of a thin wall? Is this something that's just driven by mechanical stresses or other features that are just pulling on the structure, why this moves out, or is this related to any other issue on the machine, why this distortion occurs? And to be honest with you, traditional simulation tools don't always provide this comprehensive picture and give you the straight answer of hey, this is a buckling issue, and that's your that's your solution to fixing it. So the reason why this is still somewhat complicated, you can identify where the issue is, what it is, but you still need some experience in terms of uh well how do I categorize this failure mechanism? How does this distortion really originate? And when once you find this out, then you have a path to a solution because depending on what type of or what causes a distortion, there's different ways to address this. To give you a very concrete example, uh a possible solution is just changing a thickness of a wall. A wall, a very thin wall that starts to buckle, you can add a thicker wall that immediately will address to a certain extent the buckling. It may not eliminate that, but it will address it. Another way to address buckling are adding support structures. Um and then nowadays there's this very, very commonly applied strategy which is referred to as counter deformation, which works really, really well. I want to say for a majority of cases, where you simulate the distortions beforehand, and then in very, very simple terms, I mean it's not that easy, but in very simple terms, just to give you an idea how this works, is you negatively scale or invert the simulated displacements, and then you send the distorted part onto the printer. And due to the tendency to distort while it's in the printer, when you take it out, it actually assumes its intended shape. So it's a beautiful uh technique that is very commonly applied in in additive manufacturing simulation that can address a lot of problems. But then you have those issues where let's say the thin walls with bucklings that you cannot address with counter deformation. And there you need still the expert knowledge to identify, hey, this is a buckling problem. I will probably not be able to address this with counter deformation. So, where do I go from here? And this is where I'm really excited about and the tools that we're developing here at Nodivac, is that AI has the capability to aid in this investigation of okay, I have a simulation results that tells me there's a distortion. And AI, if you feed it the correct information and prime it for a conversation for metal additive manufacturing, this is where it can be very useful in figuring out where does it originate, what's The sort of category, why it happened, and what can you do to fix it?
SPEAKER_01So I am so glad you brought that up because that was something I wanted to explore with you as well. You have, you know, I'm going to call it a uniquely human bottleneck right now, where yes, I can run the simulation, I can have results, but so what? Tends to be still a human answer in terms of how do I mitigate or fix those issues. How are you feeding or how should people think about AI in terms of getting that human expertise enough of that in there? That then you can leverage all the benefits of AI combined with the benefits you're talking about, which are uniquely human and related to experience. How are you using those together to accelerate the use of this tool in this space?
SPEAKER_00Um Yeah, I can give you a global overview on how AI is applied in the field that we are operating in. So there are groups and companies that pursue the use of AI to actually replace the simulation part. Simulations currently with the finite element method are based on mathematical relationships that are being described in the background, and you computer solve those um equations to determine a certain outcome, let's say displacements. So that's based on physics models. Um you translate into equations, numerical applications, and then you simulate that and you get a result back on your screen. One way to utilize the eye is to replace this physics-based approach with models that are either trained to produce the physics or a combination of both. So, for example, a phrase that some may be more familiar with is um physics trains neural networks, um, where you can use the true capability of AI to have the prediction of distortional stresses, um, potentially failures directly done on the actual problem. That's one part of it. And it's a very interesting field. Why? Because um simulations, uh, when you do it traditionally, like we do with uh the finite element method, they take some time. And I'm talking about now that computers have become so fast and computational power is really affordable these days. We talk in minutes and for very, very large models, maybe we talk in a couple of hours where you have to run this physics-based finite element simulation in the background. If you have a well-trained AI model, a physics-based model, the um computational time instead of hours can be reduced to sometimes seconds. If and it can be very accurate if it's very, very well trained. I think right now, where this type of technology is, it's still, I don't want to say in its infancy, but it's I don't think it has full industrial maturity because it truly matters how you train the models. And as soon as your actual case deviates from what your uh AI model was trained on, the results won't are very likely not gonna be precisely what's gonna happen in reality. So that's one of the downsides, but I think it's a super interesting field that we certainly keep our eye on it because the benefit of having a prediction almost instantaneously is just a great idea. And you know, everything that's fast is preferable, well, fast and accurate, obviously, is preferable to something that takes several minutes, maybe an hour, to come up with a prediction, right? Now, the way we bring AI into all of this is actually what you just mentioned, uh, and that is um we we try not to replace the physics part, but rather provide a what we like to call a co-engineer. So it's a trademark product that we have that is basically your simulation expert in-house that helps you investigate the results and helps you make really precise recommendations on how you can address this issue. And how did we even figure this out? This was needed. So, like I told you, I've done a lot I've done lots of customer projects, and it started to become obvious that I became the bottleneck or colleagues of mine that had an expertise on a specific topic and the customer needed to chat to them, and so they had to become available. And not only are you know it was an availability issue, but also it's costly. You know, if you have to pay an engineer that gives you expertise, feedback, they need to sit down, need to understand your case, they need to look at the simulation, and you know, you have to pay those engineers typically with a PhD or a master's degree by the hour, and it's just very costly. So when we looked at this, we started thinking, how can we get rid of this bottleneck and how can we scale this? And for a while we've been looking into generally the question, what does AI mean for us and how can we utilize it for us and provide it to our customers to help them with their cases? And that once we started sitting down and kind of reviewing this, it became obvious, hey, there's this real bottleneck, you know. Every time somebody calls us, we need to arrange a meeting and then we need to look in the case. It's just expensive, time consuming. Let's see if AI can do this for you. And when we started to develop a um prototype of linking the AI to the simulation, giving it access, priming the AI conversation for the very specific case you're working on in our tool, it became obvious this is a great solution and it it is really of great use and helps you to understand your case and gives you recommendations that I, as a you know, PhD engineer with over a decade uh of experience in additive manufacturing simulation would give you. So when we discovered this, we thought, okay, this is just a really, really great tool that can help um users the same way it helped us with our customer cases. So that's our view on AI. Keep in mind there's different ways and different um applications how AI can be applied. The one we're focusing on is not doing the simulation, but helping engineers, helping companies understand what the simulation means and how you need to change your designs to make sure the results showing from the simulations are being addressed in your build preparation before you send it to the machine.
SPEAKER_01Love that idea of a co-engineer because as I'm thinking about you have the simulation software and now you are you're interjecting AI and you're getting all these efficiencies in that whole workflow. Where do you think the human expertise still matters the most?
SPEAKER_00Oh yeah, it's interesting. Um, I can tell you simulation, and again, I'm a strong advocate for simulation, but it's still met with quite a bit of resistance. Um often by users that have a lot of hands-on experience, they still tend to be a little skeptical. But in all fairness, I see why they're sometimes skeptical towards simulation because they they bring also several decades of experience to the table, and their take on this is um, hey, I don't need to do simulation, I have a gut feeling on how this is gonna turn out because I've printed similar parts and I don't really need this. And to answer your question, where the human component comes in, in my opinion, it is actually really perfect for these types of engineers that already have years of experience because when they do get this gut feeling, hey, I'm not sure 100% if this is going to work, I'm gonna invest five minutes to run a simulation just to make sure that my gut feeling is right. Obviously, there's the other group as well that you know believes the simulation and and wants to do it no matter what, whether it's a beginner or a seasoned expert, but the human component where you use your best judgment, your experience for parts that you have already printed and failures that you have already experienced. That is something I don't see AI replacing soon, and that's not what we're intending. So, what we're intending is really that you have a colleague that you have a conversation with in our chat window, and they help you to understand what the numerics, what the results means from a you know, simulation and physics point of view. And that should be an additional tool to kind of comfort you and strengthen what you thought was going to happen in the first place.
SPEAKER_01Yeah, I I love that. Um, and I think we we share a very similar philosophy. I mean, whether you're talking about additive manufacturing or AI or software simulation, it's like how do the people use their time for the best and highest value? That's really all it is. There's no replacement, it's being able to put the highest value on that, which is a very common lean manufacturing print uh principle, you know, in the in this idea of waste and value. And so I can really appreciate that. With since you've worked with so many customers and so many industries, you know, which maybe industries or types of customers would you say are ahead of the curve on simulation, and which industries or types of customers do you think need it most that are not ahead of the curve with simulation?
SPEAKER_00Yeah, I love this question because uh it kind of shows um yeah, yeah, it's the right question to ask. So the ones that are really ahead of the curves are the bigger companies with larger budgets. So everybody at the moment that builds larger rocket aerospace parts has to certify parts. Um, companies in the automotive industry. And I mean, I don't need to drop any names here. A simple Google search of people in the industry will know the big players. For them, it's easier to allocate budget to buy traditionally very expensive simulation software for metal additive manufacturing. And due to the nature of larger companies already or having in in a lot of cases dedicated simulation teams, they can invest in research and typically process cases that are, I want to say, two to three years ahead of the rest, you know, of average players that try to produce standard parts. Um, and that involves you know applying simulation top uh methodologies to investigate very specialized materials that are attractive to the requirements of uh aerospace application or defense applications or even medical applications. So that investment shows in terms of utilizing simulation and of course other research tools and experiments to understand how we can make the 3D printing process better, how can we make better parts? I want to say they're typically a couple years ahead of everybody else, and they're um utilizing simulation. And for them, I never need to explain to them why they need simulation. They come to me with questions and they know, so that they use it, they know why they want to use it. It's more a question of hey, do you think this is the right modeling approach? Or how would you model this specific problem? So it's a different conversation with them. Um, they know their stuff. It's more like providing consultancies for modeling approaches and ping-ponging ideas of how I would do certain things. Now, the larger group um that let's say um is a service bureaus that have outputs print still print complicated parts. Um, some of them have simulation tools, but for them, simulation tools and affording simulation tools, I think is often a budgetary problem. Simulation tools are not cheap. Um, again, we are trying to change that a little bit with having very low-cost products that makes it easier for shops that have one or two machines that um still have one, two, three failures per year that you can avoid by using simulation. Um, but I feel like there's this underserved market for you know um users that have machines but not the budget to buy fifteen to you know fifty thousand dollar simulation tools. So there they typically, you know, they can probably utilize a couple of thousand dollars, maybe several hundred dollars to buy simulation tools, but not you know, twenty-thirty thousand dollars per year to to to invest in that. So those users, smaller shops, um basically working on on producing parts, they typically understand why simulation is necessary and what it can do. But practically, and again, this uh my experience also having to do a lot of sales calls with with clients that uh are interested in simulation but not quite sure whether they should invest, is there's this debate on should I truly invest. So let's say you can provide a price that is attractive to them for simulation software, there's still a lot of hidden costs that they have to be mentally prepared to swallow, and that is you need somebody to train on the software. And no matter how simple you design your predictive tool, there there will be some basic training required and some general introduction in even with our co-engineer that can help with a lot of that. You also need to show people how you know to use that. So there's this kind of mental hurdle of how do we adapt this? Well, we need to train this person on it. We have that expense, even if the chop software is very affordable, we still um need to invest in that. And then we meet we need to make sure that the person preparing the build is actually using the software. And so all these factors, in my opinion, lead to the fact that uh smaller companies and medium-sized companies are still a little hesitant because of this adoption hurdle with the aforementioned reasons. Um now I still believe that in the future this is going to change when we look at how traditional simulation, structural simulation, um grew in in adoption in automotive or in powertrain industry. It started with the bigger companies and trickled down to smaller companies using simulation tools as well. And I truly believe this will happen in metal additive manufacturing as well. And our mission is to make that happen by having a very, very attractive pricing model that works even for small companies and making it simple enough to not having to worry about training that within one or two hours of introduction you can use this and you're good to go. And then having this AI tool that helps you remembering things when you need to and helps you double-checking your um your assumptions about the simulation to get rid of this scary thing of, oh wow, how do I use simulation? Do I have to be an expert? And I think that's essentially what we're aiming for, and the benefit for for smaller companies and mid-sized companies for adapting simulation. Yeah. And I I also think the more additive manufacturing moves into cereal production. So from I mean, that's already happening now. It it I truly think there is very good cases out there where larger companies move from individual parts to what I would consider serial production. And whenever you have that, quality uh control becomes a bigger discussion. And I see simulation as well as as part of this quality control pipeline that you make sure somebody at least looked into how well can this part, uh how well can this part be printed, and are there any issues that will reveal themselves once the part goes into service.
SPEAKER_01I think that's so important when you think about not only, I mean, there's so much talk in defense and elsewhere about building up our industrial base. And that is not the five Fortune 10 companies. You know, that's the swath that you're talking about of small and mid-sized manufacturers where this idea of investment isn't just in the tool, it's in the personnel, and and you have that going in parallel with what you're talking about, where we're moving into serial production and being able to address both those needs. I really applaud what y'all are doing. You know, we've covered so much ground today. If you were to approach a manufacturing leader tomorrow and they were to take one thing away uh from this whole conversation, what would you want it to be?
SPEAKER_00I would encourage everybody, whether it's an OEM, a service bureau utilizing manufacturing, or really anybody that is somehow in the vicinity or neighborhood of additive manufacturing, simulation can be a wonderful tool that can really help you to make your process better. It may seem scary, it may seem that you have to overcome using another software tool in your process. But if you truly dedicate some time to understanding what it is, what it does for you on the long run, it will save you time, it will save you, as a consequence, costs. And 100% I'm convinced that it will lead to improvement of your manufacturing process and the parts that you're manufacturing. I have absolutely no doubt about that.
SPEAKER_01I think that is a flawless place to conclude this conversation, and it's a great takeaway for a lot of folks that listen to this that may be on the fence in terms of can this really provide ROI? And you've laid out such a wonderful business case in addition to the technical case. So I appreciate so much you spending some time with us today and and talking about simulation software and where it can add value. It's been a wonderful conversation.
SPEAKER_00Thank you very much. Uh, also kudos to you and your team. I think you're doing a great job. Um, and I I like your dedication to podcasts because I like to listen to them. That's how I discovered you guys. And I think you had great guests that I've met at trade shows that I've spoken to. Uh, and I hope you keep this up because it's just wonderful. You know, when when I ride in my car, it's along a car ride. I put on your podcast, and hey, there's this guy that I met at the trade show that year. And uh let's listen to him. What does he have to say? And then it just, you know, for myself, it generates ideas. I understand how people think about this industry that I'm very passionate about. And thank you for sharing my perspective on what I would like to at the moment still consider to be quite niche. Um, it's not, you know, simulation is not everywhere, but I'd like to think it's growing. Users more and more understand what's going on. And I hope I can I could contribute with my insight to some of the myths around simulation and how it can be adopted.
SPEAKER_01Well, you absolutely did that and more. All right.
SPEAKER_02One of the things that obviously was key is Christian made a compelling uh point around the business case for simulation. Tell me about you know how you think that should be fitting into the conversations that our customers or our customers that are considering additive have.
SPEAKER_01I think a lot of times we think about ROI as, well, I'm printing faster, so I'm saving time, and I can do more iterations. And that's true, but sometimes a bigger ROI is preventing expensive failures. And that was really his point about leveraging simulation from a business point of view, where every downstream failure becomes exponentially more costly. And this helps those manufacturers transition from call it reactive troubleshooting, why didn't this work? What should I change to proactive manufacturing? That is the key to scale production.
SPEAKER_02You know, you make such a great point. And just to, you know, when we think about manufacturing best practices, the further a par a subassembly is down the line, the more you've got invested and the more expensive a failure is. And understanding where to apply simulation and other tools, um, essentially where are the failures most costly can be a tremendous guide. You know, he also, like a lot of our guests, spent a lot of time talking about AI. What stood out to you, though, in this conversation, maybe differently from the way AI has been brought up in our other episodes?
SPEAKER_01I actually loved how he referred to AI as a co-engineer. And so when he was talking about its utility, we've had a lot of conversation around AI won't replace people, you know, it'll it'll help people be more effective. But he said this accelerating expertise, rather than pretending expertise isn't needed and outsourcing that to a tool is the big difference. And if you're a co-engineer, there implies some interaction, some insight, some exchange of ideas, and that's where he sees it being best applied in his own company, but in the simulation writ large. For a long time, the bottleneck is you would take a lot of expertise to know how to set up the simulation, and then three days of a PhD to interpret the analysis. And he said, you can't replace that interpretation necessarily, but oh my gosh, could I be more effective in setting it up? And could I at least have some analysis that is done? So now I am drawing insights into so what, what do I go do with this? Not this huge litany of issues, huge accelerant. And so for forever AI, I will be referring to as my co-engineer.
SPEAKER_02Well, you know, that's exciting to everyone except real engineers like me who don't the thought of a co-engineer. Now I gotta argue with somebody about what the right answer is. Um who do you think ultimately benefits the most from what Novek is building?
SPEAKER_01Yes, it's a great question. I asked them something similar, but I just said, you know, who do you think is benefiting the most from from simulation pure. And who should be that isn't? And he said, like many tools, when you have a Fortune 500 or a large aerospace company, they have a lot more resources at their disposal. So they will naturally be first adopters of this type of technology. But when you think about as a percentage of my investment, who is going to reap the biggest reward or suffer the greatest consequence, it's a small and mid-sized manufacturer. Every bet they place is hugely critical to their success. And so in his view, this is who should be adopting it. And if the cost is prohibitive, meaning I can't have two full-time PhDs on staff, tools like this can help lower that barrier to entry because you're doing some of that co-engineering work via a tool where before you'd have to have it via full-time FT FTEs.
SPEAKER_02You touched on something there that I think is so important and often overlooked. And that is the smaller the company, the better your batting average has to be.
SPEAKER_01Yes.
SPEAKER_02Right? If I am, you know, I don't know, alphabet, Toyota, pick a pick a large company, how many bets are they making every month, every quarter, every year? Hundreds and hundreds and hundreds. If you are a company, you know, that is a you know, ten million dollar, hundred million dollar, five hundred million dollar company, you're making a relatively small number of bets. And the further you get down that size scale, the more you're counting on those bets to work. And I think his point around, again, what's the importance of investing where there are a lot of small and medium-sized business owners who I think would do well to consider, you know, your strengths are agility, ability to tailor a customer experience more easily for different customer types. But how do you how do you play to those strengths and then minimize the fact that my batting average has to be better than a lot of my larger competitors? And I think it's a great tool to improve that. We spend a lot of time talking about how to go from pilots kind of prototyping with additive into full-blown production at scale. How do you think simulation fits into that equation based on what he shared?
SPEAKER_01Yeah, you know, when you think about what is required, production requires predictability. And predictability requires understanding your process. And a lot of times when you think about simulation, sure, it's failure prevention, but it's really a quality control tool. It's to help ensure that you have that consistent process. Um, and it's no different than inspection or process validation. And so I think that I have confidence that I understand my process and it'll work before I even hit print is huge. And we're talking about simulation, and it's used most often in the metal world, but that same mindset we have our VP here at B9 Creations always talks about building quality into each stage. That doesn't mean once you start building. That's even in the pre-build stage. And so I really appreciated his point around predictability and process and how this is such a vital part when you're having something that is very expensive, time and money, if you don't take the time to figure out what your failure points are.
SPEAKER_02Agreed. Couldn't have said it better. Um, you know, this question we've started to ask if one of our listeners only took away one thing from this episode, what do you think that should be?
SPEAKER_01Yeah, it was great talking to someone who uh doesn't live in the hardware and material space. Not those aren't critical, but we've talked a lot about uh with guests about the future of additive, you know, you need better technology, better materials, but really when you boil down to it, you need a smarter workflow. And that's what I would take away from this. And his work sits at the intersection of that. And I think it's if I have a smarter workflow that reduces scrap, makes me more money, helps improve repeatability, where I love that AI combination is then how can I democratize it and make it more accessible? So smarter, more accessible workflows, applying that methodology across everywhere you are leveraging advanced manufacturing tech is probably my one takeaway from that conversation.
SPEAKER_02And I think that relates back quite a bit to the small and medium-sized businesses thinking about where they should be leveraging this to address some of the natural vulnerabilities of a smaller business. Um I wanna I want to touch on something that's coming up for everybody who is you know following along at home. So many of these conversations touch on the topic of workforce. And the lady behind the curtain, Nicole, had a great idea that we ought to do a series on workforce, like we did a series on Made in America. And so we're putting together, at this point, not quite sure if it's gonna be three, four, or five parts, but talking to policymakers, educational experts, industry experts around this topic because we hear it all the time, but so many times you hear you hear a word too many times, it kind of loses its meaning. We're gonna peel that onion and and dig deep into that in a in some future episodes and stay tuned.
SPEAKER_01Love it. One of the biggest takeaways from this conversation is that simulation isn't really about software, it's about confidence. And confidence that your build will succeed before you spend thousands of dollars on materials and machine time, confidence your process is repeatable and as you move towards production. And ultimately, I think there's this idea that confidence needs to happen for additive manufacturing to be a reliable process rather than a series of expensive experiments. So we thank Christian so much for joining us and sharing his technical expertise and his vision for making this simulation more accessible to manufacturers everywhere.
SPEAKER_02Thanks again for joining us on this episode of the Additive Advantage Podcast. If you enjoyed this conversation, be sure to follow this show on Apple Podcasts or Spotify, subscribe on YouTube, and connect with us on LinkedIn so that you never miss a new episode. Until next time, keep building, keep learning, keep finding your additive advantage.
