Jan/Feb, 2024
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tech.com AI and AM: A Powerful Synergy... Continued from previous page Lasers produce soot when
they melt metal powder material inside an AM build chamber. During this process some of the material vaporizes and condens- es into very small particles that can occlude the lasers as they target the powder bed. The solu- tion for this is to provide a con- stant flow of inert gas to sweep away the soot as it’s generated. Sometimes, however, parti-
cles can escape this flow and land on the windows through which the laser light enters the chamber, causing contamination and heating that can distort the window itself.
Going Bigger Velo3D had already thought
through the optimum gas flow for its bigger machines’ build chambers overall. But it knew that the longer powder bed, greater interior volume, and tighter packing of more lasers would be a challenge when creat- ing optical window nozzles for the XC system. It was anticipat- ed that the amount of soot gener- ated by the new machines would be about four times as much as the original ones. The company first tried
some in-house computational fluid dynamics (CFD) simula- tions, then outsourced to a soft- ware provider as well, but the results fell short of their per- formance expectations. Velo3D requested PhysicsX
to design and simulate a solu- tion. PhysicsX has deep experi- ence in simulation, optimization and designing for tight packages, plus proprietary simulation-vali- dated tools that can automatical- ly iterate on designs using machine learning/AI-based sim- ulations. The PhysicsX approach
involves creating a robust loop between the CFD, generative geometry creation tools and an AI controller to train a geometric deep learning surrogate. The surrogate’s speed, producing high-quality CFD results in under a second, is then exploited with a super-fast geometrical generative method in another machine learning loop, which deeply optimizes the design towards whichever multiple objectives the engineer decides are important. The fidelity of the deep learning tools and robust workflow enables a highly accu- rate solution for final validation of the results against the validat- ed CFD model. In the Velo3D window noz-
zle case, a number of metrics were used to automatically quan- tify the fraction of the recirculat- ing flow within the argon curtain that was traveling upward towards the window. PhysicsX benchmarked the Sapphire win-
CAD images of a patent-pending Velo3D Sapphire AM system window nozzle (top) andCFD analysis showing consistent flow distribution (bottom).
dow solution at the start of the project, then applied its propri-
etary AI/machine-learning soft- ware, and ran huge volumes of
simulations to optimize the final design. This resulted in a nozzle design that produced the opti- mum Argon curtain flow, while working within the manufactur- ing envelope of the additive machine. The intricacy of the final
turning-vane design would be a challenge to many conventional AM systems, but the Sapphire machine’s ability to 3D print extremely thin, smooth and low- angle vanes delivered the geome- try that allowed the nozzles to function as intended. The final
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