Project uses Artificial Intelligence to control DED in repair of wear parts

January 27, 2022

For complex geometries, such as on this blade tooth, or where wear is uneven, AI-based process optimisation will enable significant gains in efficiency (Courtesy Apollo Machine and Welding Ltd)

A German-Canadian consortium has been established to improve the quality of wear parts when repaired using laser-based Directed Energy Deposition (DED) Additive Manufacturing processes. Under the project, Artificial Intelligence Enhancement of Process Sensing for Adaptive Laser Additive Manufacturing (AI-SLAM), the team are jointly developing software that can be used to run the DED processes automatically.

In Germany, the collaboration partners include Fraunhofer Institute for Laser Technology ILT, Aachen, and the software developer BCT from Dortmund. In Canada, the project is coordinated by the National Research Council of Canada NRC. A team from McGill University, Montreal, is responsible for the research side of the project, while Braintoy, Calgary, is involved in programming the machine learning algorithms. Apollo Machine and Welding Ltd in Alberta are participating in the project as an industrial service provider for DED.

Canadian machine builders are reported to receive many orders from the mining and petroleum industries for repairing wear parts. For example, rock crusher teeth used by the mining industry need to be regularly overhauled. Using the laser-based DED process, industries can apply new layers to the worn part until the original geometrical shape has been reconstructed.

Wear parts like this stone crusher tooth with an outer diameter of about 140 mm are restored with the DED process. Thanks to AI, the processes for repairing irregular surfaces shall be optimised (Courtesy Apollo Machine and Welding Ltd)

A problem with this repair process is the part’s uneven wear, which means that layers of varying thickness have to be applied. An operator must measure this after each coating step or at least after every tenth layer and readjust the process.

The project partners aim to automate this stage. For this purpose, the system automatically records geometries during the coating process, detects deviations from the specified contour and readjusts process parameters, such as the feed rate.

The optimised control parameters are calculated with the help of Artificial Intelligence. The software analyses a larger dataset and independently learns how to iteratively improve the process. The most recent milestone in the three-year project was commissioning the software functionality for both scanning components and automatic path planning at the Fraunhofer ILT facility.

The Canadian partners are continuing to develop the DED technology for repair companies such as Apollo, which uses several tons of material annually for the repair of wear parts – such as the rock crusher tooth. Accordingly, the expectations for efficiency gains through automated process control are high.

The AI-SLAM project will run until March 2024 as part of the 3+2 funding programme with Canada. The programme is funded on the German side by the Federal Ministry of Education and Research and on the Canadian side by the NRC.

www.ilt.fraunhofer.de

bct-online.de

https://nrc.canada.ca

www.mcgill.ca

braintoy.ai

www.apollomachine.com

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