Project ramping up LMD productivity using AI

Share this on social media:

Rock crusher teeth need to be regularly overhauled using LMD in the mining industry. (Image: Apollo Machine and Welding)

A German-Canadian consortium is looking to tackle productivity challenges in additive manufacturing using artificial intelligence.

The consortium is developing new software for process control to automatically optimise laser material deposition (LMD), making it considerably more productive.

The work will have relevance to applications such as those in the mining industry, where rock crusher teeth need to be regularly overhauled.

Using LMD new layers can be applied to the worn part until the original geometrical shape has been reconstructed.

However, the part’s uneven wear means that layers of varying thickness must be applied, which requires an operator to perform measurements after each coating step, or at least after every tenth layer, and consequently readjust the process.

The partners of the AI-SLAM project (Artificial Intelligence Enhancement of Process Sensing for Adaptive Laser Additive Manufacturing) are therefore jointly developing software for plant manufacturers that can be used to run LMD processes automatically. 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 Fraunhofer ILT – one of the German members of the consortium, the other being software developer BCT. Over in Canada, the project is coordinated by the National Research Council, while a team from McGill University is responsible for the research side of the project. Software firm Braintoy is involved in programming the machine learning algorithms, while the LMD industrial service provider for the project is Apollo Machine and Welding.

The Canadian partners are continuing to develop the LMD 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 teeth. Accordingly, the expectations for efficiency gains through automated process control are high.

The AI-SLAM project will run until March 2024 and is funded by the German Federal Ministry of Education and Research and Canada’s National Research Council. Potential applications of the optimised LMD process span a wide range; from the mining and energy sectors to the automotive industry, telecommunications, construction and infrastructure management.

Navigation

Navigation

Navigation

Navigation

Navigation

Navigation

Dr Michael Jarwitz, of the University of Stuttgart’s IFSW, highlights the need for a single tool capable of highly versatile and adaptable manufacturing

31 August 2022

Hot-fire testing of a GRCop-42 L-PBF chamber and NASA HR-1 LP-DED nozzle with integral channels at the NASA Marshall Space Flight Center. (Image: NASA)

03 February 2022

Dr Michael Jarwitz, of the University of Stuttgart’s IFSW, highlights the need for a single tool capable of highly versatile and adaptable manufacturing

31 August 2022

The University is hiring a new research fellow as part of a five-year project looking to move beyond ‘fixed’ fibre lasers currently used in manufacturing. (Image: ORC)

10 August 2022

ScaleNC offers digital services on a cloud platform to sheet metal manufacturers, for example, to program their machines. (Image: Trumpf)

21 July 2022

Laser powder-bed fusion can be used to efficiently manufacture a wide range of components cost-effectively. (Image: TUM)

14 July 2022

Buildings, launch pads and roads could be built out of moon dust melted with a laser, turning the lunar surface into a vital outpost for further space exploration. (Image: Team SEArch+/Apis Cor)

24 June 2022