AI helps NRC researchers find solutions to complex vehicle corrosion challenges

- Ottawa, Ontario

A research team from the NRC is installing sensors under a trailer to assess the corrosion resistance of materials for the automotive industry.

Stringent requirements for reducing fossil fuel consumption have led to vehicle manufacturing strategies that are creating new challenges—including corrosion.

Your new fuel-efficient car could be made from a combination of heavy steel and lightweight materials such as aluminium or composites. While it is eco-friendly, faster and lighter than any other car you've owned, the blend of materials (alloys) can trigger new types of corrosion. This can arise between 2 dissimilar metals, in gaps between parts or in stressed components. Lightweighting, a process which aims at manufacturing products that are less heavy, also raises new corrosion issues in larger vehicles such as trains, buses, trucks and trailers.

While vehicle construction has traditionally been steel, lighter-weight metals such as aluminium are now used as an alternative, but unfortunately cannot completely replace it. When such materials attach to steel, it opens the door to corrosion, especially when the joints come into contact with destructive elements such as winter de-icing salt.

As demand for more efficiencies increases and new materials enter the manufacturing mix, the vehicle industry is focussing more than ever on thoroughly understanding how those materials behave so they can make them more sustainable.

However, existing tools for analyzing these new forms of corrosion have fallen short. Common tests in the past were spraying saline solutions onto metal samples in corrosion chambers, but these could not reproduce emerging types of corrosion that arise from using innovative combinations of metals. They also did not provide real-world examples of what happens on the road in different conditions.

Focussed on metal fabrication and specialized in digital manufacturing, the National Research Council of Canada's (NRC) METALTec industrial R&D group saw in this challenge an opportunity to put forward its expertise. Led by the NRC, the group, which aims at improving metal manufacturing processes and products, is leading the research to find innovative solutions to this industry problem. "We realized we needed new tools to find solutions to these problems," says Marc-Olivier Gagné, Metallurgy and Corrosion Scientist at the NRC. "We started with resources that were commonly used in mechanical engineering but had never been applied to corrosion science." These tools include digital simulation, machine learning (ML) and artificial intelligence (AI).

Driving the solution

To build reliable AI- and ML-based tools, one needs reliable data. This is why corrosion researchers at the NRC's Automotive and Surface Transportation Research Centre decided to install different types of sensors and samples on vehicles as they drove through real road conditions. Gagné reports that 2 test vehicles now in use—a 26-foot cube truck and 53-foot trailer—collect data on temperature, humidity, wetness, surface temperature and more.

"We can follow each vehicle using data loggers and modems that send masses of data to our networks at the NRC," he adds. "This gives us a complete portrait of the real road conditions and provides us with information to build simulation models that help predict corrosion in different scenarios and combinations."

In addition to these simulation models, our objective is to bridge the gap between real-world corrosion and actual lab experiments to meet clients' needs for real-world statistics. To do so, the team established a database of variables (set points) such as temperature, humidity and salt spray against which to measure metals performance in the NRC's Aluminium Technology Centre labs located in Saguenay, Quebec. "A new, custom corrosion chamber was built last year and we are using the data from the sensors on the road to calibrate it. We started with 50,000 lines of data but that was too much for the chamber to handle," he says. "I then crunched the numbers in a machine-learning model that built clusters of data and boiled those down to a manageable size for the chamber—50 new set points. This is where machines came to help. We used a clustering algorithm to shrink the original dataset to only a handful of data points that are still able to accurately reproduce the real-life environment."

To help them understand the nature of the chemistry in alloys that causes corrosion, the NRC research team invited Janine Mauzeroll, professor, McGill University Department of Chemistry to collaborate with them. "They needed to identify the alloy microstructures that initiate corrosion and quantify the extent of corrosion damage they generate," she says. "They then feed this data into their AI model to predict an alloy's longevity and maintenance requirements."

Seizing the future

Mauzeroll reports that this close collaboration is a good illustration of how government and academia can build long-lasting relationships to not only benefit industry, but also help students expand their experience and research skills. "It is important to train people at all levels, because when everyone on a team contributes to the research, they all get excited and want to see it through."

She also appreciates the fact that the NRC has developed strong industrial relationships over the years that provide opportunities for bridging their collective skills. "In this case, the NRC's close relationship with the industry will speed up the development of solutions, because this industrial R&D group of more than 25 companies will be able to test and modify them on actual vehicles."

According to Gagné, the data can help vehicle and parts manufacturers to accurately assess if certain assemblies will be prone to corrosion. This means using NRC technology to customize and test their designs to increase safety, lower costs and extend vehicle life. "We have also seen interest from other countries to adapt their real-road tests to hot-weather extremes as well."

With so many disciplines working on the case and industry helping with the field testing, Gagné foresees the technology being available on the market within the next 5 years, since simulation and ML applications have already reached a good level of maturity. The NRC's new corrosion chamber however has started to be tested not too long ago. "The advantages to industry of this approach is that they don't have to build simulators or develop code; they just have to drive the car."

Led by the NRC's METALTec industrial R&D group, which aims at building a research community that catalyzes innovation in the metal fabrication sector, this initiative demonstrates how collaborative work with industry and academia can help solve manufacturing problems. To get access to the complete results of this project or to join the METALTec industrial R&D group, interested businesses and organizations can contact David Prud'homme, Business Development Officer.

This project is funded by Natural Resources Canada and the Centre québécois de recherche et de développement de l'aluminium (CQRDA).

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