Real-time layer height estimation during multi-layer directed energy deposition using domain adaptive neural networks

Yang, Liu; Sohn, Hoon; Ma, Zhanxiong; Jeon, Ikgeun; Li, Peipei; Cheng, Jack Chin Pang

doi:10.1016/j.compind.2023.103882

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Their findings indicate that the ANN model did not produce satisfactory results, but they hypothesized that the limited size of the datasets could have been a factor. Analogous to the prior study, Yang et al [192] predicted layer height-the averaged difference in the current and previous deposition height of the predetermined cross-sectional profiles. A set of fixed variables in each layer, including laser power, scanning speed, feedstock rate, and layer index, was used.…”

Section: Data-driven Approachmentioning

confidence: 95%

“…Perani et al [191] developed a vision-based monitoring system for the DED molten pool by utilising a laser coaxial CMOS camera that could accommodate the entire melt pool, due to its 400 × 400 pixel size. Yang et al [192] employed a visual camera for gauging the scanning velocity of the deposition head. This was performed as the traverse speed frequently slowed down at corners and differed from what was intended.…”

Section: Vision-based Signalsmentioning

confidence: 99%

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Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

Imran,

Che Idris,

De Silva

et al. 2024

Technologies

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This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.

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Section: Data-driven Approachmentioning

confidence: 95%

Section: Vision-based Signalsmentioning

confidence: 99%