Email: chena17@rpi.edu
BS, Rutgers University in Mechanical Engineering, 2020
PhD student, Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace and Nuclear Engineering
My current research interests are in fault detection systems for metal additive manufacturing. I currently examine both thermal and acoustic response signals for fault classification.
(Additive Manufacturing, Process Control, Control Systems)
Current Work
- Simple Time-Frequency domain algorithm based on spectrogram implemented and used for classifications
- Generalizations to multiple raster geometries assessed
- Interpretable classification framework developed
Figure 1: Schematic representation of the proposed AM monitoring approach.
Figure 2: Test layer (Long, 3n+ 2) spectrogram (middle) with detected faults from time-frequency method (red, top) and reconstruction error (bottom). Orange regions (a and b) are temporal faults, whereas the red region (c) indicates a spacial fault
Alvin Chen is a fourth year PhD student at Rensselaer Polytechnic Institute. He is affiliated with the Intelligent Structural Systems Laboratory (ISSL) and the Intelligent Systems, Automation and Control Laboratory (ISAaC) where he combines elements of non-destructive evaluation with advanced additive manufacturing processes to detect faults in-situ for metal AM. His current areas of interest are additive manufacturing, process control, and control systems. He holds a BS from Rutgers University in Mechanical Engineering