Trumpf AI assistant uses camera to improve laser cutting edges

The company’s researchers cut thousands of parts to train its new AI assistant.

Farmington, Conn.-based manufacturing technology company Trumpf is introducing a new “Cutting Assistant” application which uses artificial intelligence to help users improve the quality of laser-cut edges.

Production employees just take a picture of their component’s cut edge with a hand scanner. Then, the AI assesses the edge quality, evaluating it using objective criteria such as burr formation. With this information, the Cutting Assistant’s optimization algorithm suggests improved parameters for the cutting process. Then the machine cuts the sheet metal once more. If the part quality still does not meet expectations, the user has the option to repeat the process.

This solution is available for all TruLaser series laser cutting machines purchased as of May 2025, which feature a power output of 6 kW or higher.


“The Cutting Assistant is a great example of how AI-enabled tools can help overcome problems related to the skilled worker shortage and also saves time and money. When it comes to productivity, this application creates a competitive edge for fabricators,” says Grant Fergusson, Trumpf Inc. TruLaser 2D laser cutting product manager.

AI makes optimization suggestions

When laser cutting, materials that are not optimized for laser cutting often produce edges with wide variations in cut quality, forcing production employees to constantly change the technology parameters. This involves adjusting each individual parameter one by one— a process which demands a lot of time and employee experience. By integrating the Cutting Assistant into the machine software, optimized parameters can be transferred seamlessly into the software without programming.

While developing the Cutting Assistant, Trumpf experts cut thousands of parts and drew upon many years of expertise, using their extensive knowledge to train the software’s algorithm. This work on the Cutting Assistant did not stop on its release—data from applications in the field will also be incorporated into the solution to enable faster and more reliable results.

Written by

Michael Ouellette

Michael Ouellette is a senior editor at engineering.com covering digital transformation, artificial intelligence, advanced manufacturing and automation.