Unveiling the future of unique and functional parts: AI-driven 3D printing.

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In the near future, advancements in AI technology could lead to an increase in the production of unique and practical 3D printed parts. One startup, called “PhysicsX”, is already exploring the potential of AI and simulation engineering to revolutionize machine and product design in advanced industries, with a specific focus on areas that impact the climate and human health.

PhysicsX has made significant progress in the field of 3D printing through a partnership with Velo3D. The 3D printer manufacturer was facing a challenge with soot build-up on its observation windows, which conventional flow simulation tools were unable to solve. However, PhysicsX was able to come up with a solution using their AI and machine learning technology.

Founder and co-CEO of PhysicsX, Robin Tuluie, explained their approach: “We started by benchmarking the Sapphire window solution and then used our proprietary AI/machine learning software to run extensive simulations and optimize the final design. This resulted in a nozzle design that generated the ideal Argon curtain flow, all while working within the manufacturing constraints of the additive machine.”

While this achievement is impressive, PhysicsX believes their technology can be applied even more directly to additive manufacturing by helping generate 3D models that are efficient and practical for printing. Using AI tools, simulation times can be reduced from hours to seconds, with deep learning algorithms automatically evaluating and modifying the geometry of a part to achieve specific outcomes. This allows for the creation of designs that prioritize attributes such as lighter weight, stress reduction, fluid flow optimization, heat exchange, durability, part consolidation, and more.

So how does this work? According to Tuluie, AI solves the CFD (Computational Fluid Dynamics) or FEA (Finite Element Analysis) equations in a non-traditional manner. Instead of focusing on every individual math problem, machine learning examines and emulates the overall physical behavior of a design. This approach requires fewer computational resources while still providing a robust evaluation of the design in every relevant environment. In fact, hundreds of thousands of design candidates can be simulated and evaluated within a day.

The impact of this technology is comparable to generating a detailed artwork in seconds using AI, rather than having an artist spend days creating it. When such technology becomes commonplace, which PhysicsX hopes it will, we can expect a surge in generative parts for 3D printing. What sets PhysicsX apart is its ability to create 3D models that meet both functional needs and are printable. Imagine being able to specify requirements and have a system generate a fully printable model in just seconds. In a few years, this could become the standard approach.

The possibilities presented by PhysicsX’s technology are truly exciting. As additive manufacturing continues to advance, we can anticipate a future where complex and customized 3D printed parts are easily and efficiently generated, thanks to the power of AI.

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