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As efforts associated with the exploration of multi-principal element alloys (MPEAs) using computational and data-intensive methods continue to rise, experimental realization and validation of the predicted material properties require high-throughput and combinatorial synthesis of these alloys. While additive manufacturing (AM) has emerged as the leading pathway to address these challenges and for rapid prototyping through part fabrication, extensive research on developing and understanding the process-structure-property correlations is imminent. In particular, directed energy deposition (DED) based AM of MPEAs holds great promise because of the boundless compositional variations possible for functionally graded component manufacturing as well as surface cladding. We analyze the recent efforts in DED of MPEAs, the microstructural evolution during the laser metal deposition of various transition and refractory elements, and assess the effects of various processing parameters on the material phase and properties. Our efforts suggest that the development of robust predictive approaches for process parameter selection and modifying the synthesis mechanisms are essential to enable DED platforms to repeatedly produce defect free, stable and designer MPEAs.
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Digital Fringe Projection for Interlayer Print Defect Characterization in Directed Energy Deposition
Abstract Directed Energy Deposition (DED) is one of the main additive manufacturing (AM) families, enabling the fabrication of multi-material parts with high material addition rates. However, the incremental nature of DED fabrication makes it prone to local defect formation due to process condition fluctuations. Known for its rapid and precise 3D surface measurement capabilities, digital fringe projection (DFP) was previously demonstrated in process monitoring for powder bed AM. This study brings DFP to the DED process through development of a custom motor stage system and validates its effectiveness in assessing surface topography and build height measurement. Measurements were taken on both correctly deposited builds and builds with off-nominal deposition conditions, where the system was able to detect pitting as small as 0.425 mm in the lateral size and 0.154 mm in depth in the case of reduced laser energy. This work paves the way for future machine learning-enabled interlayer defect identification, classification, and healing via altering subsequent processing settings.
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- Award ID(s):
- 2216298
- PAR ID:
- 10544529
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8788-2
- Format(s):
- Medium: X
- Location:
- Seattle, Washington, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/ISFA2024-141314
