Latest / Hardware Startups with Fexingo: Devices, Robotics, and Manufacturing Tech Companies / How Hardware Startups Use Digital Twins to Cut Field Failures
Transcript
- Lucas: So Luna, I was reading about an agricultural robotics startup that used a digital twin to simulate ten thousand hours of field operation before they ever built a production unit. And their field failure rate dropped by forty percent. Luna: Forty percent? That's huge. How did they pull that off with a digital twin? I thought those were mostly for big aerospace and automotive companies. Lucas: That's the assumption, right? But the tools have gotten way more accessible. This team built a virtual model of their robot arm — every joint, motor, sensor, and the control software — and ran it through simulated seasons of planting and harvesting. The digital twin let them see where the arm was overheating or wearing out prematurely, and they changed the design before committing to tooling. Luna: So they're catching failures that would have shown up only after months of field testing. That's a huge advantage when you're on a tight startup timeline. Lucas: Exactly. And the beauty is, you don't need a massive simulation department. There are off-the-shelf tools now, and even open-source physics engines. The startup I'm talking about had a team of maybe five engineers. They spent about three months building the model, and that investment paid for itself in avoided prototype iterations and warranty claims. Luna: But is a digital twin really accurate enough to catch things like an overheating motor? I'd think you'd need very detailed physics and thermal models. Lucas: You do, but you don't need perfect accuracy everywhere. The trick is to focus on the failure modes that actually bite you. For that robot arm, the main issues were bearing wear and motor temperature. So they modeled those specifically, using data from off-the-shelf components. They didn't try to simulate every bolt and wire. Luna: So it's about picking your battles. Model the things that are most likely to fail, and get those right. Lucas: Right. And the model gets better over time. Once you have physical prototypes, you feed real sensor data back into the twin, so it continuously improves. That's what makes it a 'digital twin' rather than just a one-off simulation. It's a living model that stays connected to the actual product. Luna: That sounds like a lot of data. How do small startups handle the data pipeline? Isn't that a whole other project? Lucas: It can be, but there are platforms that handle the backend for you. You just integrate your sensors and push data to the cloud. And you don't need to start with real-time streaming. Even batch uploads of field data, say once a day, are enough to update the twin and spot trends. Luna: So it's not an all-or-nothing thing. You can start simple and add sophistication as you go. Lucas: Exactly. And that's the shift we're seeing. It used to be that digital twins were a luxury for companies with deep pockets and dedicated simulation engineers. Now, with cloud computing and affordable sensors, a five-person hardware startup can get value from a digital twin on its first product. Luna: That's a game changer. What about the cost, though? I imagine the software licenses and compute time can add up. Lucas: There's a range. Some open-source tools are free, but you trade off support and ease of use. Commercial platforms might run you a few thousand dollars a month for a small team. But compare that to the cost of a recall or a failed field deployment. For the ag robotics startup, avoiding just a handful of field failures paid for the entire digital twin setup. Luna: That makes sense. It's like insurance, but better, because it also helps you improve the design proactively. Lucas: Exactly. And it doesn't just help with failures. You can also use digital twins to test different operating environments, like extreme temperatures or dusty conditions, without having to ship prototypes to those locations. That kind of flexibility is huge for a startup trying to expand into new markets. Luna: I can see how that would de-risk expansion. But I wonder, are there any downsides? Like, could you over-rely on the twin and skip physical testing entirely? Lucas: That's a real risk, and I'd push back on any startup that thinks a digital twin replaces physical testing. You still need to validate your models with real hardware. The twin is a complement, not a replacement. The best approach is to use the twin to narrow down what you need to test physically, making those tests more targeted and efficient. Luna: So it's about using the twin to focus your physical testing where it matters most. That sounds like a smart use of limited resources. Lucas: Right. And that's what the best hardware startups are doing now. They're treating the digital twin as a core part of their development process, not an afterthought. And it's becoming a competitive advantage. If you can catch a design flaw before you spend money on tooling, that's a huge edge. Luna: I'm curious about the tools themselves. Are there any that are particularly friendly for hardware startups? Or is it more about the approach? Lucas: There are platforms like Ansys, COMSOL, and SimScale that have simulation capabilities, and some now offer digital twin modules. But there's also a new wave of startups themselves building digital twin tools specifically for product teams, with a focus on usability and integration with CAD and sensor data. It's a fast-moving space. Luna: So it's not just big incumbents. There's a whole ecosystem forming around this. Lucas: Absolutely. And that's good news for hardware founders because it means more options and lower prices. But it also means you need to be careful about choosing a tool that fits your specific use case and not just following the hype. Luna: That's a good point. What about the skills side? Do you need a simulation engineer on the team to make this work? Lucas: It helps, but it's not strictly necessary. Many founders are learning the basics themselves. There are online courses and lots of documentation. The key is to start with a narrow use case and build from there. You don't need to model the entire product on day one. Luna: So it's about incremental adoption. Start small, prove value, then expand. Lucas: Exactly. And that's what I love about this trend. It democratizes a capability that used to be the domain of giants. And it's not just about avoiding failures. Digital twins can also help you optimize performance, reduce maintenance costs, and even create new revenue streams through predictive maintenance services. Luna: That's an interesting angle. Once you have a digital twin of your product in the field, you could offer monitoring as a service. Lucas: Right. And that's where the data becomes a business asset, not just an engineering tool. For a hardware startup, that could be a differentiator in a crowded market. Luna: This is genuinely useful. I can see why so many hardware founders are starting to adopt this approach. It's practical, cost-effective, and forward-looking. Lucas: And on that note, if this kind of conversation is useful for what you're building or running, there's a quiet way to help us keep making it. A small group of listeners already chips in monthly through buy me a coffee dot com slash fexingo. That's what keeps this show ad-free and independent. Luna: Yeah, it's a simple way to support the work we do, and every bit helps us keep digging into topics like this. Lucas: So, going back to the digital twin story — the ag robotics startup is now expanding into a new market, and they're using the same twin to simulate conditions in different soil types and climates. They didn't have to send a physical prototype to each location. They just ran the simulations, made a few adjustments, and built a version they're confident will perform. Luna: That's a huge advantage. It speeds up their go to market and reduces the risk of a costly failure in an unfamiliar environment. Lucas: Exactly. And as edge AI chips get cheaper and sensor data becomes more abundant, the fidelity of these twins is only going to improve. We're heading toward a world where every product has a digital shadow that evolves with it, and that's going to change how we think about reliability and maintenance. Luna: It feels like we're at a turning point. Digital twins are becoming a staple in hardware development, not just a niche tool. Lucas: I think you're right. And for founders, the message is clear: start small, focus on your critical failure modes, and let the twin earn its keep. It might just save you from a field failure that could sink your startup. Luna: That's a great takeaway. Thanks for breaking this down, Lucas. It's given me a lot to think about for any future hardware projects. Lucas: And that's the goal. If you're building hardware, give the digital twin a shot. It's more accessible than you think, and the payoff can be enormous.