ENBIS-23 Valencia Conference

Session

INVITED QSR-INFORMS

12 Sept 2023, 11:05

2.7/2.8

Conveners

INVITED QSR-INFORMS

• Kamran Paynabar (1H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology)

158. A Novel Low-Dimensional Learning Approach for Automated Classification of 2-D Microstructure Data in Additive Manufacturing

Dr Marco Grasso (Politecnico di Milano, Department of Mechanical Engineering)

12/09/2023, 11:05

Quality

Novel production paradigms like metal additive manufacturing (AM) have opened many innovative opportunities to enhance and customize product performances in a wide range of industrial applications. In this framework, high-value-added products are more and more characterized by novel physical, mechanical and geometrical properties. Innovative material performances can be enabled by tuning...

138. Maximum Covariance Unfolding Regression: A Novel Covariate-Based Manifold Learning Approach for Point Cloud Data

Kamran Paynabar (School of Industrial and Systems Engineering)

12/09/2023, 11:35

Other/special session/invited session

Point cloud data are widely used in manufacturing applications for process inspection, modeling, monitoring and optimization. The state-of-art tensor regression techniques have effectively been used for analysis of structured point cloud data, where the measurements on a uniform grid can be formed into a tensor. However, these techniques are not capable of handling unstructured point cloud...

122. Automated Registration of Polarized Light Microscopy Images Using Deep Learning Techniques

Nathan Gaw (Air Force Institute of Technology)

12/09/2023, 12:05

Other/special session/invited session

Studies have identified a connection between the microtexture regions (MTRs) found in certain titanium alloys and early onset creep fatigue failure of rotating turbomachinery. Microtexture regions are defined by their size and orientation, which can be characterized via scanning electron microscopy (SEM) Electron Backscatter Diffraction (EBSD). However, doing so is impractical at the...

123. An Adaptive Sampling Strategy for Real-time Anomaly Detection with Unmanned Sensing Vehicles

Ana Estrada Gomez (Purdue University)

Unmanned sensing vehicles (USVs) have been widely used for real-time anomaly detection in various applications, including environmental monitoring, precision agriculture, and military surveillance. The USVs collecting data can only provide partial information about the space being monitored. Thus, it is critical to decide where to deploy the USVs at each point in time to maximize the change...