Fraunhofer ILT and partners use Artificial Intelligence and Additive Manufacturing to repair mining tools
February 27, 2025
Researchers from the Fraunhofer Institute for Laser Technology ILT in Aachen, Germany, have teamed up with project partners to develop an artificial intelligence module to help with laser-based Directed Energy Deposition (DED) Additive Manufacturing, a process referred to as Laser Material Deposition (LMD). The Fraunhofer team and project partners, including research organisations and companies from Canada, are working to extend the life of mining tools.
Often, used mining tools (such as excavator buckets with worn teeth and dull chisels and crushers) are melted down and replaced – an expensive and resource-inefficient method. In the ‘Artificial Intelligence Enhancement of Process Sensing for Adaptive Laser Additive Manufacturing’ (AI-SLAM) project, metal powder and a laser beam are used to restore the original contour of parts.
“The goal of the project was to automate all the steps, from mapping the defects to planning the paths and parameters to be used during welding and then to the actual execution and quality control,” stated Max Zimmermann, the project manager responsible for LMD coating and heat treatment at the Laser Material Deposition department at Fraunhofer ILT.
Project partners are the National Research Council of Canada, McGill University, and companies including Calgary-based AI firm Braintoy and Apollo Machine and Welding, which is based in Edmonton. BCT, a software development company based in Dortmund, is also involved.
Ultra-hard tungsten carbide and stainless steel
During the repair process, the laser optical system follows a pre-calculated path over the tool’s surface. Stainless steel is melted at approximately 1300°C and deposited, while nozzles simultaneously aim jets of tungsten carbide particles at the same area. The particles combine with the molten steel to form an ultra-hard coating on the tool after cooling, protecting it against wear and corrosion.
One of the challenges for the team of researchers was to find the optimum ratio of tungsten carbide particles to steel. “Too high a proportion of particles makes the coating brittle and prone to cracking, but with too much stainless steel, it’s too soft, so it wears down quickly,” Zimmermann explained.
The laser power also has to be calibrated so the temperature is high enough to melt steel but not so high that the tungsten carbide particles melt as well (at approximately 2900°C). If that were allowed to happen, the tungsten carbide would become too soft.
There are many other parameters, including the distance between the nozzles and the surface, the speed at which the system traces its path, the overlap between paths, the power of the laser and much more. In all, there are 150 parameters to set and coordinate when planning a single repair process.
AI plans and controls repairs
For the AI-SLAM project, the Fraunhofer researchers developed a several-part AI module that governs this complex planning and control process. As the first step, a line laser uses a CMOS camera to capture the worn contours of the tool to produce an image of the current surface geometry. The image is then compared against the contour of the new component, which is also stored in the software.
Finally, the module uses the difference to calculate the path and thickness of the metal coating that should be applied. A camera feeds images to the AI during the coating process so it can detect any discrepancies or errors during manufacturing.
Much faster and less error-prone
Project partner BCT incorporated the researchers’ AI module into its OpenARMS operating software, which translates the parameters recommended by the AI for the welding process into control commands. This means human operators no longer need to type in the machine codes, a time-consuming and error-prone step. Braintoy, Calgary, Canada, is responsible for the machine learning algorithms. It also provides the platform for data analysis in the DED machine.
All of the solutions work together, so the repair process is able to take place automatically and without errors once the operator presses the start button.
The experts from Fraunhofer ILT will present the AI-SLAM project at Hannover Messe from March 31 to April 4, 2025. Attendees can watch a software demo of laser material deposition at the Fraunhofer booth (Hall 2, Booth B24).
