The Manufacturing Executive · 2 Jan 2024 · 37m · Episode 186
Hugo Nordell, founder and CEO of Encube, explains how his software addresses the gap between design intent and manufacturability by leveraging AI to predict CNC machining costs and climate impact at the point of design. He argues that decades-old engineering software paradigms are failing modern manufacturers due to a severe talent scarcity, where new engineers lack shop-floor experience, and rapidly changing supply chains driven by reshoring and sustainability legislation. Nordell details how Encube’s platform uses machine learning to simplify complex simulations and provide real-time feedback on manufacturability, aiming to unlock 10x productivity growth for companies like Volvo and major aerospace contractors.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Engineering & simulationInCube
The company uses AI to analyze designs for CNC machining in real-time, providing visual insights into manufacturability issues, required machine types, and cost drivers while the engineer is still designing.
“we leverage multiple different types of AI to help companies, virtually in real time, while they're designing a part that's gonna be made with scenes in machining. We tell them, here's the issues, here's the level of complexity.”
Speaker 3 · Hear it at 26:56
Engineering & simulationdeep learning and AI
The speaker describes using deep learning and AI to accelerate engineering simulations, such as finite element analysis or computational fluid dynamics, reducing simulation time from days to seconds.
“in matter of seconds, you can actually use deep learning and AI to simulate, for instance, you know, the bending of a rod, or, you know, the heat dissipation of within a given type of material, that would today, even with, you know, very sophisticated kind of high-performance compute clusters, might take the ace to simulate.”
Speaker 3 · Hear it at 26:56
early-stage companies
Other shows on the same ground, closest first.
Marc Kermisch, CTO and AI Officer at Protolabs, explains how the company uses AI and simulation to turn CAD files into physical parts in under 24 hours across CNC machining, 3D printing, injection molding, and sheet metal. He details how ProDesk flags manufacturability issues like tight tolerances and ejector locations, while internal AI tools optimize print box placement and visual quality inspection. Kermisch also discusses the critical tradeoffs between prototyping and production, highlighting common design mistakes such as non-uniform wall thickness and missing draft angles that engineers must address before tooling.
Brian Thompson, head of Creo CAD software for PTC, discusses three key 2023 product development trends for hardware startups: using design simulations earlier in the process, integrating design for manufacturing directly into CAD to avoid context switching, and leveraging additive manufacturing for short-run production. He explains how these tools reduce risk and cost by allowing designers to explore more alternatives and get real user feedback before committing to expensive tooling. The conversation highlights how software advancements make simulation and DFM accessible to non-experts, enabling faster iteration and better product quality.
Nick Pinkston, founder and CEO of Plethora, explains how his company automates CNC machining to provide real-time design feedback and instant pricing, aiming to make manufacturing as easy as pizza delivery. He details why hardware startups often fail by not engaging manufacturers early enough to understand cost trade-offs and production constraints. Pinkston also discusses the growth of the San Francisco Hardware Startup Meetup, which he co-founded, and shares lessons on scaling operations, hiring management early, and maintaining organizational culture between factory floor and office staff.
Oscar Lovera, COO at Xometry, explains how the AI-powered manufacturing marketplace is digitizing the sourcing of custom parts by integrating design, quoting, and manufacturability feedback into a single workflow. He discusses the shift from traditional, disconnected RFQ processes to instant, scalable matching between engineers and a global network of manufacturers, including CNC, 3D printing, and injection molding partners. Lovera emphasizes that while AI automates repetitive tasks, human expertise remains critical for complex problem-solving, trust-building, and ensuring operational excellence, arguing that strong processes matter more than flashy equipment. He also shares his perspective on supply chain resilience, advocating for global flexibility over local sourcing and highlighting the importance of intentional career choices for future engineers.
Lin Kayser, CEO of Hyperganic Group, explains how his company uses advanced algorithms to shift engineering from a visual, manual process to a code-based software paradigm. He details how this approach allows engineers to generate highly optimized, complex objects on demand, particularly benefiting additive manufacturing by enabling rapid iteration and superior performance in applications like heat exchangers and bioprinting. Kayser argues that this technology can supercharge engineers, bridge the manufacturing skills gap, and transform global trade by shifting the focus from shipping physical components to licensing algorithms for production in localized, automated factories.
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