R&D

Researchers collaborate to optimise steel classification process

| Author / Editor: Friederike Meyer zu Tittingdorf / Rosemarie Stahl

While practised experts are able to detect steel microstructures correctly in 50 percent of the cases, the machine learning method achieved an accuracy of 93 percent.
While practised experts are able to detect steel microstructures correctly in 50 percent of the cases, the machine learning method achieved an accuracy of 93 percent. (Source: Oliver Dietze)

About 5,000 different types of steel are currently available on the market. How can steel producers guarantee that a particular steel always has the same quality? Scientists have now developed a method that is supposedly more accurate than conventional control procedures.

Up until now, experienced experts analysed material samples of steels under the microscope and carefully compared the results against reference images. But classifying materials in this way is not free from error. Using machine learning techniques, computer scientists and materials scientists in Saarbrücken have now developed a method that is much more accurate and objective than conventional quality control procedures. Their results have just been published in Scientific Reports.

“It took a fair amount of time before the computer scientists had understood why the internal structures of a material and their representation in image form play such an important role for materials scientists,” says Dominik Britz, PhD student in the Department of Functional Materials at Saarland University. These internal structures are important because they are very closely linked with the properties exhibited by the material. For Seyed Majid Azimi, Britz's research colleague at the Max Planck Institute for Informatics, the job was first to produce significantly more accurate results than any of the image analyses conducted manually by expert materials scientists. To achieve such results, Azimi feeds his high-performance computer with image data that was previously “hand-classified” by experts. This data is used to train the computer models and these models are subsequently tested by comparing them against additional sets of human-classified image data.

“We see this as just the beginning of a close cooperative partnership with Saarbrücken’s highly respected computer science research teams. The new deep learning methods will not only help us assess the quality of steel more objectively and more accurately, we also anticipate that our results will be transferable to many other production processes and materials,” explains Professor Frank Mücklich.
“We see this as just the beginning of a close cooperative partnership with Saarbrücken’s highly respected computer science research teams. The new deep learning methods will not only help us assess the quality of steel more objectively and more accurately, we also anticipate that our results will be transferable to many other production processes and materials,” explains Professor Frank Mücklich. (Source: Maximilian Schlosser)

“Manufacturing special steels is an extremely complex process that depends on many individual factors including the chemical composition of the material, the rolling process used and the types of heat treatment that the material is subjected to. Every stage of the production process influences the internal structure of the steel,” explains Dominik Britz. Materials scientists refer to this internal structure as the material’s “microstructure”. The microstructure is composed of “grains”, each of which is a tiny crystallite with a particular crystal structure. But neighbouring grains also differ in terms of their spatial orientation and, additionally, in terms of their individual shapes and their spatial connectivity, resulting in microstructures of high geometrical complexity. “These extremely complex structures can be made visible during the material development and the quality control stages by taking microscopic images. Specially prepared samples are evaluated using optical and electron microscopy,” explains Britz.

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Much better than the naked eye

Classifying a material involves comparing these microscope images with reference images that exhibit a typical geometrical microstructure. Over time, experienced engineers in company quality assurance departments develop a discerning eye that enables them to decide which particular steel microstructure they are dealing with. “But even these practised experts will sometimes make an incorrect call, as the differences between the images are sometimes barely discernible with the naked eye. Although humans are pretty good at distinguishing small relative differences, we are not very good at recognising absolute geometric standards,” explains Professor Frank Mücklich, who supervised the study. Mücklich is also Director of the Steinbeis Materials Engineering Center Saarland (MECS) in Saarbrücken, whose staff were involved in the study.

The materials scientists were interested in finding an objective procedure that would be far less prone to user error and that could be applied irrespective of the user’s level of expertise. “Machine learning methods allow computers to recognise complex patterns very rapidly and to assign the geometry of the microstructures in microscope images. They can learn the features of previously classified microstructures and compare these with recognised patterns,” explains Mücklich. Using this approach, the research team in Saarbrücken was able to determine the microstructures of low-carbon steel at a level of accuracy that was not previously possible. “When using our system for microstructural classification, we achieved a level of accuracy of around 93 percent. With conventional methods, only about 50 percent of the material samples are correctly classified,” says Mücklich.

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