Statistical and Data-Driven Methods for Additive Manufacturing Qualification: Proceedings of a Workshop (2024)
Chapter: 6 Recap of Day 1
6
Recap of Day 1
To start the workshop’s second day, the moderators of the three panels from the first day summarized the key points from each of those panels.
Maria Emelianenko began with her recap of Panel 1. She started with a list of threats and industry needs, such as the need for new materials to operate under extreme environments. She also pointed to the needs for a focus on manufacturability and reproducibility, for the development of a scalable pipeline to test the mechanical performance of digital twins, and to deal with uncertainty in microstructure characterization. She closed with a list of challenges and opportunities. These included putting more effort into standardizing data and creating benchmark datasets to share outside the national laboratories, moving toward a reliable model of developing and validating digital twins, and linking data pulled from the additive manufacturing (AM) process with feedstock data, post-processing characterization data, and lifetime performance data for use in digital twins.
Next, Teresa Clement identified some key points from Panel 3, which was focused on post-processing issues. First, she said, using integrated computational materials engineering requires having data that are integrated and translatable across multiple processes and, more importantly, a digital thread that models the entire process from raw materials to finished product. This digital thread, she continued, makes it particularly vital to have data integrated across all of the processes. Using digital threads and machine learning, it should be possible to assess for defects in real time and make necessary repairs in situ. Last, taking advantage of
the flexibility that AM processes offer will require being flexible in post-processing as well.
Last, Ade Makinde offered a recap of Panel 2. The main points he identified included the use of in situ sensing data to aid in improving the quality of additive parts, the use of process maps and rapid qualification tests to optimize build parameters, and how Pratt & Whitney is qualifying parts made with AM.
