A version of this story first appeared in Industrial Heating.
Inclusions are unavoidable by-products of steel-making. Microscopic particles arising from different chemical reactions and processes can vary widely in size, shape and composition, and have important effects on the material properties of steel. Inclusions have been a focus of industry for more than a century, and remain so in both production settings and academic research.
“Inclusions are these little particles floating around in the steel that are always there,” said Bryan Webler, a professor of materials science & engineering (MSE) at Carnegie Mellon University. Solid-phase inclusions can clump together to clog nozzles and other flow control systems that mediate the flow of liquid steel. Some inclusion chemistries reduce ductility, resistance to fatigue or overall toughness in steels. “They affect the final performance of the steel, which is why we care about them so much.”
Webler and fellow MSE Professor Liz Holm have turned to computer vision and machine learning techniques to study steel inclusions, with the goal of making characterizing the microscopic particles faster and less expensive. They shared their initial findings at the 2019 conference of the Association for Iron & Steel Technology.
Inclusion analysis typically relies on two inputs: images from scanning electron microscopy, which are analyzed to give information on size, shape and location, and energy-dispersive spectroscopy (EDS) to identify chemical composition. In production settings, it takes a trained metallurgist a few hours to characterize the inclusions from a sample of steel.
Decreasing that turnaround time, however, could give steelmaking operations tighter control on product quality and raw materials usage. Consider calcium treatments, another focus of Webler’s research. Calcium can be added to the melt to form calcium-aluminates, converting solid alumina inclusions into liquid droplets, reducing the risk for clogging nozzles. However, adding too much calcium forms undesirable solid calcium sulfide inclusions. Using information on the inclusion population, operators can tune how much calcium they add to achieve just the right level. Did calcium sulfide inclusions form? Turn down the calcium. Too much alumina? Add more. The speed at which this analysis is performed can impact the bottom line of the operation both in material costs and process performance.
Computer vision, an advanced image processing technique that relies on machine learning, is becoming ubiquitous: from facial recognition on smartphones to character analysis for converting old written works to digital texts. Increasingly, computer vision and machine learning are being used in materials science. Holm has used these techniques to classify carbon nanotubes, predict stress hotspots and characterize powder feedstocks for 3D-printing. Inclusion analysis, an area rife with images, naturally lends itself toward computer vision, Holm said.
Future of the field
Computer vision may be able to improve how inclusions are analyzed in a handful of ways.
First, there are always non-inclusions (e.g. dust, holes, scratches) in scanning electron microscopy images that can be wrongly detected as inclusions (false positives). Even when classified correctly, analyzing false positives is still wasted time for the EDS system, and filtering them out would speed up the process. Further, if computer vision could determine the chemical composition of inclusions right from scanning electron microscopy images alone, EDS could be eliminated entirely, reducing equipment and labor costs.
Even more appealing to Webler, though, is the idea that computer vision might elucidate unknown information about inclusions. In other fields, computer vision has been able to produce insights invisible to human interpretation.
Bryan Webler demonstrating use of SEM for inclusion analysis.
“Computer vision systems can ‘see’ traits such as age, gender and even cardiological risk factors in retinal image,” said Holm, who studied the benefits of algorithms. “No ophthalmologist had previously uncovered any of those traits by looking at human eyes. By turning computer vision loose on inclusion images, the team hopes that new insights might be generated, learning through the intricate differences in the images what might be hidden within inclusion populations.”
The initial results from Webler’s and Holm’s research are encouraging. From the scanning electron microscopy images they analyzed, they determined with 98% accuracy whether a feature was an inclusion or not. Already, integrating computer vision into current systems to filter out non-inclusions would likely save time in EDS scans. Differentiating each feature as an inclusion or not took their algorithm 70 milliseconds. Making that same determination using EDS takes more than 14 times as long (roughly 1,000 ms).
Interestingly, Holm said, it was not clear to her by looking at the scanning electron microscopy images why their computer vision tool classified features in one group or the other. Yet, that it did so with a high degree of accuracy holds promise that more information may still be lurking in the scanning electron microscopy images. The next step, they say, is to “classify inclusions by chemical composition based only on [backscattered electron] images,” potentially eliminating the need for EDS.
Though he acknowledges that the work is still in its early phases, Webler is optimistic about his team’s approach, and how it could eventually have a positive impact on the industry.
“We hear lots and lots about big data, industry 4.0, all of these things, but these techniques are still opaque in a lot of ways,” he said. “But this is one example, at least, where I can see how machine learning techniques could be useful when they are applied in this specific way.”