Speaker
Description
Arc-based Directed Energy Deposition (DED-Arc) provides significant potential for large-scale additive manufacturing, yet industrial adoption remains restricted by the underutilization of multi-axis system kinematics and a lack of comprehensive data for robust process control. This study investigates the complex interdependencies between torch orientation, stick out length, and welding parameters to establish a foundational database for AI-driven quality assurance. By employing a multi-sensor approach, the research systematically evaluates the thermal behavior, process stability, and resulting geometric accuracy of deposits produced under varying operational conditions. The experimental design focuses on a wide parameter window, specifically covering torch angles from -45° to 45° and stick-out variations from 5 mm to 35 mm. Experimental results indicate that these specific parameter variations significantly alter melt pool dynamics, heat input distribution, and the localized thermal history, which directly impacts the resulting microstructure, phase distribution, and mechanical integrity. Preliminary data suggest distinct correlations between specific parameter sets and quantifiable microstructural properties, including hardness profiles and grain morphology. Furthermore, the integration of high-frequency acoustic emission analysis demonstrates promising potential for the reliable real-time detection of process instabilities, such as arc fluctuations or geometric deviations. By bridging the gap between sensor data and process outcomes, this research defines a scalable methodology for future predictive process control models and demonstrates how leveraging the full kinematic flexibility of DED-Arc systems enhances manufacturing quality and reliability.
| Speaker Country | Germany |
|---|---|
| Would you like to publish your paper in the special issue of BHM "Berg- und Hüttenmännische Monatshefte" | No |