Terra Quantum and Empa Develop AI Model for Laser Welding Prediction

Insider Brief

• Terra Quantum and Empa developed LP-FNO, a Fourier Neural Operator model that predicts three-dimensional temperature fields and melt-pool boundaries from laser welding parameters.
• The model was trained on high-fidelity simulations of Ti-6Al-4V across laser powers of 40–190 watts and scan speeds of 0.1–1 meter per second.
• Once trained, LP-FNO generates full 3D predictions in about 8 milliseconds at standard resolution and 88 milliseconds at twice the resolution.

PRESS RELEASE — Terra Quantum, a global leader in quantum technologies, and Empa, the Swiss Federal Laboratories for Materials Science and Technology, announced LP-FNO (Laser Processing Fourier Neural Operator), an artificial intelligence surrogate model that predicts full three-dimensional melt-pool dynamics in laser welding up to 100,000 faster than traditional multiphysics simulation. Removing the computational bottleneck that has long blocked real-time process control and digital twin deployment in industrial laser processing.

The Simulation Bottleneck in Laser Welding

Laser welding is a precision manufacturing process used across aerospace, medical device, and automotive production, where control of the melt pool, the zone of molten metal found beneath the laser beam, is central to weld quality and consistency. High-fidelity multiphysics simulations have been the primary tool for understanding and optimizing the process, but their computational cost has made real-time application impractical. A single simulation run using current gold-standard tools takes approximately six minutes at standard 10 µm resolution and over one hour at finer 5 µm resolution, placing process control, large-scale parameter optimization, and uncertainty quantification out of reach for industrial operators.