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Search Publications by: Zhuo Yang (IntlAssoc)

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Displaying 1 - 25 of 50

Knowledge Extraction in Additive Manufacturing: a Formal Concept Analysis Approach

November 13, 2024
Author(s)
Zhuo Yang, Yan Lu, Yande Ndiaye, Mario Lezoche, Herve Panetto
In Additive Manufacturing (AM), it is still a major challenge to manage part quality, which is heavily influenced by feedstock materials, process settings, and in-process control. Deviations in these factors can lead to defects in the final product

Multi-Scale Model Predictive Control for Laser Powder Bed Fusion Additive Manufacturing

November 13, 2024
Author(s)
Gi Suk Hong, Zhuo Yang, Yan Lu, Brandon Lane, Ho Yeung, Jaehyuk Kim
Additive manufacturing (AM) process stability is critical for ensuring part quality. Model Predictive Control (MPC) has been widely recognized as a robust technology for controlling manufacturing processes across various industries. Despite its widespread

Towards Reproducible Machine Learning-Based Process Monitoring and Quality Prediction Research for Additive Manufacturing

November 13, 2024
Author(s)
Yan Lu, Zhuo Yang, Jiarui Xie, Mutahar Safdar, Andrei Mircea Romascanu, Hyunwoong Ko, Yaoyao Fiona Zhao
Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). In-situ and in-process data acquired using sensors can be used to train ML models that detect process anomalies

An Overarching Quality Evaluation Framework for Additive Manufacturing Digital Twin

September 2, 2024
Author(s)
Yan Lu, Zhuo Yang, Shengyen Li, Yaoyao Fiona Zhao, Jiarui Xie, Mutahar Safdar, Hyunwoong Ko
The key differentiation of digital twins from existing models-based engineering approaches lies in the continuous synchronization between physical and virtual twins through data exchange. The success of digital twins, whether operated automatically or with

Investigating Statistical Correlation Between Multiple In-Situ Monitoring Datasets for Powder Bed Fusion Additive Manufacturing

August 24, 2022
Author(s)
Zhuo Yang, Yan Lu, Milica Perisic, Yande Ndiaye, Adnan Gujjar, Fan-Tien Cheng, Haw-Ching Yang
In-situ measurements provide vast information for additive manufacturing process understanding and real-time control. Data from various monitoring techniques observes different characteristics of a build process. Fusing multi-modal in-situ monitoring data