AI-Driven Toolpath Optimization for Wire-Arc Directed Energy Deposition

Rapid production of large-scale components has emerged as an advantage of additive manufacturing (AM). Compared to longer lead times seen with forgings and castings, additive manufacturing techniques such as wire-arc directed energy deposition (DED) achieves deposition rates up to 4kg/hour, enabling the production of large structures within hours. Wire-arc DED employs a robotic cell and the gas metal arc welding process to print complex, near-net-shape components. This robotic cell is integrated with a digital twin to program toolpaths and simulate the pre-deposition process. Currently, toolpath parameters are largely dependent on human experience and heuristic decisions. However, an optimal toolpath is essential for printing a quality component, as various scanning strategies result in distinct microstructures and mechanical properties. Furthermore, the toolpath strategy has an increased effect during large-scale printing in the thermal history of the printed part and often contributes to distortion. Thus, this work develops AI-driven strategies for determining the optimal printing toolpath within the digital twin environment. The AI tool developed predicts optimal stepover distance for AM and cladding applications. Using Design of Experiments (DOE) and statistical mapping techniques, 16 experiments were fitted to regression models, then used to generate 500 synthetic data. A neural network was then trained on the augmented data set with the objective of learning the optimal stepover for a flat deposition based on the welding parameters (wire feed speed and travel speed). Finally, a Python-based graphical user interface (GUI) was developed, allowing users to input wire feed speed and travel speed and receive an output of the optimal stepover. The system was successfully tested by cladding carbon steel plates with stainless steel. Ongoing work develops a similar AI model to determine the optimal printing strategy (raster, contour, etc.) to effectively clad a defined area by optimizing productivity and deposition quality.