A team of engineers at the University of Toronto, led by Professor Yu Zou (MSE), is working to advance the field of metal additive manufacturing at the university’s first metal 3D printing laboratory – with a focus on both SLS and DED technology.
“We are working to uncover the fundamental physics behind the additive manufacturing process, as well as improving its robustness, and creating novel structural and functional materials through its applications,” said Zou.
The team is also applying novel experimental and analytical methods to better understand the SLM and DED printing processes. Currently, their research is focused on advanced steels, nickel-based superalloys, and high-entropy alloys. They may expand to explore titanium and aluminum alloys in the future, according to the article written by the University of Toronto’s Safa Jinje.
“One of the major bottlenecks in conventional alloy design today is the large processing times required to create and test new materials. This type of high-throughput design just isn’t possible for conventional fabrication methods,” said Ajay Talbot (MSE MASc candidate).
With AM techniques such as DED, the team is rapidly increasing the exploration of alloy systems – altering the composition of materials during the printing process by adding or taking away certain elements.
“We are also working towards intelligent manufacturing. During the metal 3D printing process, the interaction between a high-energy laser and the material only lasts for a few microseconds. However, within this limited timeframe, multi-scale, multi-physics phenomena take place,” said Jiahui Zhang (MSE Ph.D. candidate). “Our main challenge is attaining data to capture these phenomena… In our research, we have successfully customized specific machine learning methods for different parts of the metal additive manufacturing lifecycle.”
In the lab, high-speed infrared camera systems are integrated directly into the metal 3D printers. The team has also built an in-situ monitoring system based on the images taken by the printer to analyze and extract the key features of printed objects.
“With the development of computer vision, a well-trained deep learning model could automatically accomplish some basic tasks that human visual systems can do, such as classification, detection, and segmentation,” added Zhang.
The team is actively working to apply machine learning and computer vision to develop a fully autonomous closed-loop controlled 3D printing system that can detect and correct defects that would otherwise emerge in AM parts. According to Zou, implementing these systems will greatly widen the adoption of metal AM systems in the industry.
Since building up the lab’s metal printing capabilities, Zou and his team have established partnerships with government research laboratories, including National Research Council Canada (NRC), and many Canadian companies, including Oetiker Limited, Mech Solutions Ltd., EXCO Engineering, and Magna International.
And in addition to novel research into additive manufacturing, Zou offers an additive manufacturing course at the University of Toronto that is available to both undergraduate and graduate students.
“Metal 3D printing has the potential to revolutionize manufacturing as we know it. With robust autonomous systems, the cost of operating these systems can be dramatically reduced, allowing metal additive manufacturing to be adopted more widely across industries worldwide,” said Zou.