3D Plasma Metal Deposition (3DPMD) advances additive manufacturing by enabling higher deposition rates and flexibility with powder materials, including recycled aluminum. Process simulation, vital for creating accurate digital twins, relies heavily on quality data. However, aluminum's unique thermal properties and emissivity present significant challenges in data acquisition and process modeling, especially under plasma-based MAM conditions.

Our study addresses these challenges by integrating a suite of sensors, including quotient pyrometers for melt pool temperature, IR cameras for cooling behavior, and spectroscopy sensors for material purity. This enriched dataset enhances process simulation accuracy, allowing for detailed modeling of aluminum's behavior during 3DPMD.

Empirical tests on two geometries validate our simulation approach, demonstrating its reliability and the potential for improved predictive modeling. This research underscores the importance of diverse data in simulations, paving the way for digital twin advancements in 3DPMD. It promises enhanced efficiency, material utilization, and product quality, broadening the application of 3DPMD for various aluminum alloys.