A research team from the Korea Institute of Industrial Technology has developed an innovative module that upgrades existing production systems with artificial intelligence (AI). This “add-on” module makes it possible to detect defects in real time and autonomously optimize production conditions. The technology aims to transform older production systems into AI-supported smart factories without having to replace the entire hardware.
The application was developed specifically for Direct Energy Deposition (DED) 3D printing, a process in which metal powder or wire is melted by a high-energy laser and built up layer by layer. As errors often occur in DED due to variables such as laser power, build-up rate or material feed, the add-on relies specifically on sensors and AI-supported error detection. If the system detects deviations, it informs the operator via a display and simultaneously adjusts critical process parameters to ensure optimal conditions.
Senior researcher Lee Ho-jin noted, “The technology detects various stacking defects using deep learning technology and actively controls the conditions of 3D printing equipment in real time,” adding, “It is expected to have a significant impact as it can also be applied to implement digital twin virtual models of production process data.”
In contrast to conventional processes, which often require experienced technicians to carry out lengthy tests, the module automates error correction, saving time and resources. This opens up new opportunities for smaller companies in particular, with limited access to specialized experts, to use modern production technologies.
The technology has already been transferred to companies such as MR Tech and Dico for commercialization.
Moon Chang-kyu, head of MR Tech, said, “It can also be applied to robot-based production processes, and we are promoting the commercialization of AI-based robot 3D printing technology.”
Dico CEO Hwang Jun-cheol noted, “It has a high competitive advantage in terms of being able to obtain and manage temperature data of production processes based on video systems,” and said, “We plan to apply it in aerospace, medical, and automotive fields.”
The module represents a significant step towards smart manufacturing and underlines the potential to integrate AI-based optimizations into existing production processes.
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