HP and NVIDIA Collaborate on Open-Source Manufacturing Digital Twin

HP three-D Printing and NVIDIA Modulus have introduced a collaboration to create an open-source production virtual dual, leveraging physics-informed device studying (physics-ML). This partnership goals to foster innovation in AI engineering programs through embedding bodily rules into the training procedure, in keeping with NVIDIA Technical Blog.

Developments in Physics-ML

Physics-ML is a burgeoning grassland that comprises bodily rules into device studying fashions, bettering the generalizability and potency of neural networks. NVIDIA Modulus, an open-source framework, facilitates the development, coaching, and fine-tuning of those fashions with a easy Python interface. The framework deals reference programs to support area professionals observe physics-ML to real-world importance circumstances.

The Virtual Dual group at HP three-D Printing Device Group has applied physics-ML fashions for his or her production virtual dual and contributed this paintings to Modulus. HP, a pacesetter in additive production, goals to boost up the onboarding of brandnew programs and undertake this generation in manufacturing environments. Dr. Jun Zeng, HP’s outstanding technologist, emphasised the virtue of physics simulation engines grounded in production procedure variability, noting the numerous speedups accomplished with well-trained physics-ML fashions.

Virtual Twins in Additive Production

HP has a lavish historical past of technological innovation, together with the advance of thermal inkjet generation. The corporate’s actual innovation, HP Steel Jet, allows the manufacturing of industrial-grade three-D steel portions. HP is growing a virtual dual for Steel Jet generation to optimize design parameters and procedure regulate, thereby bettering phase attribute and production handover.

The HP group created the Digital Foundry Graphnet style, making use of physics-ML to boost up the computation of steel powder subject matter section transitions. This style has accomplished vital speedups, enabling close to real-time, high-fidelity emulation of the steel sintering procedure. The style has additionally demonstrated its applicability to numerous geometrical designs and procedure parameter configurations.

Physics-ML Innovation at HP

Even if physics-ML remains to be in its early phases, the HP Virtual Dual group believes within the position of the open-source population in accelerating its construction. By means of open-sourcing Digital Foundry Graphnet via NVIDIA Modulus, HP has joined the physics-ML open-source population. Conventional high-fidelity physics simulations are computationally extensive, ceaselessly taking hours or days for one design iteration. Physics-ML surrogate fashions trade in high-fidelity emulation, enabling sooner design iterations.

Quick comments on product design manufacturability and automatic design screening are actually conceivable with physics-ML surrogate fashions. Those fashions additionally permit product design groups to importance prior simulation information as a supply of ground-truth information. The combination of product design and production optimizations, which historically required more than one iterations between branchs, can now be considerably speeded up.

HP’s procedure physics simulation device, Virtual Sintering, has been deployed to HP Steel Jet consumers to beef up production results. Operating a well-trained steel sintering inferencing engine takes simply seconds to procure the overall sintering deformation worth, considerably decreasing the life required for design iterations.

Empowering Researchers

Physics-ML surrogate fashions are at the vanguard of near-real-time simulation workflows. Inventions like Digital Foundry Graphnet reveal the facility of AI to boost up simulation workflows, handing over predictions in seconds. Democratizing AI for production is very important to empower a much wider field of innovators to resolve trade demanding situations.

AI researchers and the HP three-D Printing group make the most of the NVIDIA Modulus open-source undertaking to collaborate with area professionals. NVIDIA helps the physics-ML analysis population through offering a platform that complements collaboration and innovation, making sure that complex AI gear are out there to all.

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