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Measuring Overhang Down-Skin Dross Depth and the Inter-Dependency on Melt Pool for Laser Powder Bed Fusion Additive Manufacturing

August 20, 2024

Author(s)

Zhuo Yang, Jaehyuk Kim, Brandon Lane, Yan Lu

The overhang down-skin surface exhibits distinct behaviors compared to the regular up-skin surface in the powder bed fusion process. Evidence suggests that weak support could be the primary cause of these differences. Previous studies by the authors have

ENHANCING PART QUALITY MANAGEMENT USING AN HOLISTIC DATA FUSION FRAMEWORK IN METAL POWDER BED FUSION ADDITIVE MANUFACTURING

March 5, 2024

Author(s)

Zhuo Yang, Jaehyuk Kim, Yan Lu, Albert T. Jones, Paul Witherell, Ho Yeung, Hyunwoong Yang

Metal powder bed fusion-based additive manufacturing (AM) processes have gained widespread adoption for producing complex parts with high performance. However, a multitude of factors still affect the build process, which leads to great challenges in

SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING

November 11, 2022

Author(s)

Jaehyuk Kim, Yan Lu, Zhuo Yang, Hyunwoong Ko, Dongmin Shin, Yosep Oh

Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control

A MULTIMODAL DATA-DRIVEN DECISION FUSION METHOD FOR PROCESS MONITORING IN METAL POWDER BED FUSION ADDITIVE MANUFACTURING

October 20, 2022

Author(s)

Zhuo Yang, Jaehyuk Kim, Yan Lu, Ho Yeung, Brandon Lane, Albert T. Jones, Yande Ndiaye

Data fusion techniques aim to improve inference results or decision making by combining multiple data sources. Additive manufacturing (AM) in-situ monitoring systems measure various physical phenomena and generate data at different scales and sampling

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