Papers

[1] 3D Printer Fault Detection on Metaverse Environment (Thesis)
[2] Efficient CNN-based Fault Classification for FDM 3D Printer using Embedded Device (KICS Fall 2022)

Last Week's Progress

[1] - Continue the simulation (Run another method for comparison, apply to the edge device, modify unity
including set position and variable for all the data)

[2] - Submission completed

This Week Todo's

[1] - Thesis presentation (in NSL)
- Title:
1. Fault Detection of FDM 3D Printer in a Virtual Environment
2. Digital-Twin Assisted Fault Detection in Smart Additive Manufacturing
3. Fault Detection on Metaverse Environment for FDM 3D Printer

- Performance metrics:
1. Accuracy
2. Loss
3. Trainable parameter
4. File size of embedded file
5. Training time
6. Time for one prediction (inference time?)
7. Latency
8. System overhead (is it needed?)

short clip: <a href='https://drive.google.com/file/d/1G-_aYFnFGchZuBZ8RIkVNJsa_mRvrFVn/view?usp=share_link'> Click Here</a>;

Project Progress

3D Printer Monitoring

Last Week's Project

- Waiting for the new dataset for validation

This Week's Project

- Waiting for the new dataset for validation

Monthly Goals

- Finish thesis book
- Finish PPT for Thesis Defense
- Finish all the simulation and implementation part
- KICS Fall 2022

Annual Goals

- Submit domestic and international conferences (Done with KICS Summer 2022 and ICTC 2022)
- Submit journal
- Graduate