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AI and AM: A Powerful Synergy By Robin Tuluie, Founder and Co-CEO, PhysicsX I
n the digital-design-engineering world, at the foundation of innovation in advanced manufacturing, AI’s “deep learning” has
the potential to transform how the world makes products — in highly positive ways. There’s an urgent opportunity to fully
exploit the tools of computer-aided engineer- ing (CFD, FEA, electromagnetic simulation and more) using the capabilities of AI. It’s optimization like never before, automated with machine learning, at a speed and level of precision far beyond what can be accom- plished by most manufacturers today. This means quantum leaps in efficiency
and accuracy: AI tools can cut simulation times from hours to only seconds, employing deep learning to automatically evaluate, and then incrementally modify, the geometry of a part — within bounds that the user dictates — in order to create specific outcomes. The resulting, final design achieves the ideal combination of whatever attributes its mak- ers have prioritized: lighter weight, stress and fatigue reduction, optimum fluid flow, heat exchange, conductivity, durability, part consolidation, and more. AI accomplishes this feat by solving the
CFD or FEA equations in a non-traditional way: machine learning examines, and then emulates, the overall physical behavior of a design, not every single math problem that underlies that behavior. This uses far fewer computational resources while achieving an extremely
robust evaluation of the design in every applicable environment. Hundreds of thou-
ing (AM) that AI is perhaps the most comple- mentary. Machine learning can fully explore the AM-design space, identifying the true limit of every type of physics that will apply to a specific component. This unleashes AM’s unique power to deliver whatever level of geometric complexity will enable the most creative and cost-efficient solution to a diffi- cult engineering challenge. This combination of additive manufac-
turing and AI has now been successfully applied to optimize and improve the perform- ance of such disparate additively manufac- tured items as a 3D-printed heat exchanger used on jet engines, a championship-winning motorbike, the impeller blades of a cardiac pump for patients with heart failure and dozens of other applications in advanced industries. What’s more, certain AM-system makers
Flow images of one-half of the eight-window Sapphire XC, showing consistent gas flow.
sands of design candidates can be simulated and evaluated in less than a day.
Additive Manufacturing While machine learning can certainly
benefit the design of products that are pro- duced via any type of manufacturing process or technology, it’s with additive manufactur-
have also recognized the value of this capa- bility to improve their own machines — sav- ing time, boosting performance, and fine-tun- ing the accuracy of their prints. Now that advanced metal AM is produc-
ing parts certified for rockets, aircraft, and heavy industry, customer demand for larger- volume equipment has been surging. Several years ago, in anticipation of this, California- based Velo3D began designing its larger-vol- ume Sapphire XC to include eight 1,000W lasers, four times as many as its original Sapphire machine.
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