PhysicsX
Assembly Line
AI-Driven 3D Printing: Unveiling the Future of Unusual and Practical Parts
We could see an increase of very unusual yet practical 3D printed parts in the near future due to AI technology.
“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 thousands of design candidates can be simulated and evaluated in less than a day.”
AI Optimization: New Opportunities for 3D Printing
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 thousands of design candidates can be simulated and evaluated in less than a day. Bottom line: Applying AI amplifies the typical 10-20% performance improvements of simulation tools alone—up to 30% and higher. (Of course it follows that real-world testing of finished parts remains an essential task to ensure that all quality and performance metrics are met.)
Velo3D requested PhysicsX to design and simulate a solution. PhysicsX has deep experience in simulation, optimization and designing for tight packages (from considerable work in F1 racing and expertise in data science, machine learning and engineering simulation), plus proprietary simulation-validated tools that can automatically iterate on designs using machine learning/AI-based simulations. 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 accurate solution for final validation of the results against the validated CFD model.