HP has applied physics-ML to develop a digital twin for Metal Jet technology. The new AI helps process engineers predict and optimise both the design parameters and the process control parameters to improve part quality and manufacturing yield. 

As a component of HP’s digital twin effort, the HP team developed the Virtual Foundry Graphnet model by applying physics-ML to significantly accelerate the computation that predicts the metal powder material phase transition. Such a trained surrogate model has achieved orders-of-magnitude speedups to enable near real-time, high-fidelity emulation of the metal sintering process.

Virtual Foundry Graphnet has also demonstrated the feasibility of such AI surrogate models to be applied to designs of diverse geometrical complexity and different process parameter configurations. 

HP Metal Jet, enables a metal additive manufacturing system that delivers industrial-grade throughput and quality of novel 3D metal parts beyond the capability of traditional manufacturing processes.

“Our team has been developing physics simulation engines based on first principles,” said Dr. Jun Zeng, HP’s distinguished technologist heading the Digital Twin effort with HP’s 3D Printing Software Organisation.

“We bring experimental sensing and metrology data to calibrate these physics simulation engines so that they are grounded by the manufacturing process variability. With physics-ML, once well trained, we see orders-of-magnitude speedups, and the model can run on your laptop. Such near real-time prediction delivered by physics-ML opens doors for many new applications.” 

By open-sourcing Virtual Foundry Graphnet through the NVIDIA Modulus platform, HP 3D Printing has joined the physics-ML open-source community. 

Traditional high-fidelity physics simulation workflows are computationally intensive, with one design iteration often taking hours to days to complete. Using low-fidelity, reduced-order models significantly limits design exploration. Physics-ML surrogate models offer high-fidelity emulation and complement numerical solvers to enable design iterations that are faster by orders of magnitude. 

The development by HP with Virtual Foundry Graphnet have been detailed in a recent paper titled Virtual Foundry Graphnet for Metal Sintering Deformation Prediction.