Interrogation of Pixel-Level Thermal Histories of Ti-6Al-4V by Laser Powder Feed Additive Manufacturing

The business case for additively manufactured (AM) parts continues to face hurdles due to the high costs and long lead times required to qualify both geometry and constituent material properties of new parts. For complex geometries, the thermal characteristics can vary significantly at different locations, which in turn can lead to variations in the material properties throughout the part. As such, it is important to understand the relationships between part geometry, thermal history, and material properties.

An interdisciplinary team at Boeing has worked to experimentally quantify these relationships in powder-feed Ti-6Al-4V through a unique design of experiments (DOE) methodology. A total of 20 trial geometries were designed and then built on a Laser Freeform Manufacturing Technology (LFMT) powder feed AM platform, and thermal measurements were collected during the builds (infrared video, thermocouple and pyrometer data).

A graphical user interface (GUI) was developed to collate, process and visualize the large amounts of resulting thermal data, and Machine Learning (ML) strategies were applied to categorize and group geometric regions across all 20 shapes based on measured thermal characteristics. The resulting builds were then sampled for physical tensile and microstructure testing, and the results correlated back to the thermal regions identified in the ML model. Thermal measurements were also applied to validate Laser Direct Energy Deposition (LDED) simulation capabilities (3DExperience) to further drive model based engineering (MBE) capabilities for AM design and development within Boeing.

This presentation will cover the DOE, experimental setup, data processing, process simulation and results achieved. Recent data findings and next steps will also be discussed.