A research team from the University of Toronto has introduced a new optimization framework for metal-based 3D printing using Laser Directed Energy Deposition (DED). The system, called AIDED (Accurate Inverse Process Optimization), uses machine learning and genetic algorithms to automatically determine optimal print parameters, aiming to improve the quality and reproducibility of additively manufactured metal parts.
“The wider adoption of directed energy deposition – a major metal 3D printing technology – is currently hindered by the high cost of finding optimal process parameters through trial and error,” says PhD candidate Xiao Shang, first author of the new study. “Our framework quickly identifies the optimal process parameters for various applications based on industry needs.”
The method targets industrial applications with high demands for dimensional accuracy, material properties, and process reliability, such as aerospace, medical technology, and energy sectors.
“One major challenge of 3D metal printing is the speed and precision of the manufacturing process,” says Yu Zou, a professor of materials and engineering in the Faculty of Applied Science & Engineering. “Variations in printing conditions can lead to inconsistencies in the quality of the final product, making it difficult to meet industry standards for reliability and safety. Another major challenge is determining the optimal settings for printing different materials and parts. Each material – whether it’s titanium for aerospace and medical applications or stainless steel for the nuclear reactors – has unique properties that require specific laser power, scanning speed and temperature conditions. Finding the right combination of these parameters across a vast range of process parameters is a complex and time-consuming task.”
AIDED addresses this issue with a closed-loop optimization process: a genetic algorithm first generates possible combinations of process parameters such as laser power, scan speed, and thermal management. These combinations are then evaluated by a trained machine learning model for their impact on part geometry and material properties. The most promising parameter sets are reintroduced into the optimization loop until an optimal configuration is found.
“Industries such as aerospace, biomedical, automotive, nuclear and more would welcome such a low-cost yet accurate solution to facilitate their transition from traditional manufacturing to 3D printing,” says Shang.
“By the year 2030, additive manufacturing is expected to reshape manufacturing across multiple high-precision industries,” adds Zou. “The ability to adaptively correct defects and optimize parameters will accelerate its adoption.”
Looking ahead, the team aims to develop an AI-powered, self-regulating laser printing process that can detect defects in real-time and make adjustments during the build process. The research was published in the journal Additive Manufacturing, and work is currently underway to advance the system toward industrial implementation.
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