In a significant stride towards optimizing additive manufacturing processes, researchers have developed a novel approach to predict and control welding power in wire and arc directed energy deposition (waDED) for aluminum structures. This breakthrough, published in the journal *Materials Research Express* (translated to English as “Materials Research Express”), could have substantial implications for the energy sector, where large-scale metallic components are in high demand.
The study, led by Fabio Haunreiter from the LKR Light Metals Technologies at the AIT Austrian Institute of Technology, focuses on mitigating heat accumulation during the waDED process. High melting rates in waDED make it an efficient method for producing large components, but the high heat input can lead to quality issues. “By adjusting the welding power according to the component’s temperature, we can significantly improve the process and the final product,” Haunreiter explains.
The research team utilized a proportional-integral-derivative (PID) control function within the finite element software LS-DYNA to numerically predict optimized welding powers. This is the first instance of using such a control function for waDED processes. To calibrate their model, the researchers conducted experiments using tungsten inert gas (TIG) welding on aluminum substrate plates. The results were impressive, with a maximum power deviation of just 76W, or 5%, between the experimental and simulated data.
The calibrated model was then used to predict optimized welding powers for a multi-layer wall structure. The results demonstrated good seam quality and prevented variations in seam width caused by insufficient or excessive heat input. “This approach not only enhances the quality of the final product but also has the potential to reduce operating and material costs,” Haunreiter adds.
The implications for the energy sector are profound. Large-scale components used in energy infrastructure, such as those in renewable energy systems, could benefit from this optimized manufacturing process. The ability to predict and control welding power accurately could lead to more efficient production, reduced waste, and improved component performance.
This research opens new avenues for future developments in additive manufacturing. As Haunreiter notes, “The integration of advanced simulation tools with real-world processes can drive innovation and improve industrial practices.” The study’s findings could inspire further exploration into numerical power prediction for other additive manufacturing processes, potentially revolutionizing the way large metallic components are produced across various industries.