Smart metal components capable of distinguishing screw loosening risks and internal/external physical deformation factors have been developed, and are drawing expectations to lead digital transformation in manufacturing, automotive, aviation, and medical device industries in the future.

A research team led by Professor Jeong Im-du from the Department of Mechanical Engineering at UNIST (President Lee Yong-hun) announced on the 26th that it has successfully developed 'perceptive stainless steel metal components' using 3D printing additive manufacturing technology and artificial intelligence technology.

The team also announced that it has implemented a digital twin at the metal component level through the fusion of artificial intelligence technology and augmented reality technology.

This research was conducted as a joint collaboration with Georgia Institute of Technology in the United States, Nanyang Technological University in Singapore, Korea Institute of Materials Science, POSTECH, and Gyeongsang National University.

The technology developed by the research team embeds deformation sensors during stainless steel metal component manufacturing to obtain data reflecting physical conditions, and then uses artificial intelligence analysis to enable the metal component to self-detect its own condition.

This intelligent stainless steel metal component was able to self-detect the degree of loosening of surrounding fastening screws and the location of loosened screws with approximately 90% accuracy. It can even distinguish the type of object that struck it (hand, hammer, wrench, etc.).

Additionally, through the digital twin metal component, changes in internal and external stress distribution can now be confirmed in real-time in mixed reality.

In metal forming, which is primarily a high-temperature process exceeding 1,000 degrees Celsius, the technology of inserting sensors internally is extremely difficult; however, the team utilized its proprietary 'metal forming sensor insertion technology.'

L-PBF L-PBF (Laser powder bed fusion): One of the metal 3D printing methods that selectively fuses materials by irradiating high-temperature lasers on powder material. Precise printing is possible.

This is a technology that safely inserts heat-sensitive sensors into the design position through metal 3D printing processes. Additionally, to ensure that mechanical properties of the metal component do not deteriorate with sensor insertion, the insertion position was designed, and after sensor insertion, safety was verified through mechanical analysis and microstructural analysis.

Graduate student Seo Eun-hyuk, who participated as the first author, stated, "By extracting meaningful big data from within the metal and applying artificial intelligence, this is ultimately a technology that can contribute to safety and productivity improvement at industrial sites through digitalization of various metal machinery-based manufacturing industries."

Professor Jeong Im-du from UNIST, who oversaw the research as the corresponding author, remarked, "The results of this study can be applied not only to stainless steel metal components, but also to general mechanical components used in manufacturing such as general steel, aluminum, titanium alloys, and other materials, and will be able to help lead the digital transformation of existing manufacturing, automotive, aerospace, nuclear power, and medical device industries."

The research results were published on May 5th in 'Virtual and Physical Prototyping,' an international academic journal ranked within the top 7% of JCR rankings in the manufacturing field. The research was conducted with support from the 'Individual Basic Research Project' promoted by the National Research Foundation of Korea (NRF).