Summary of Virl: Volume-informed Representation Learning Towards Few-shot Manufacturability Estimation, by Yu-hsuan Chen et al.
VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation
by Yu-hsuan Chen, Jonathan Cagan, Levent Burak kara
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed VIRL (Volume-Informed Representation Learning) approach pre-trains a 3D geometric encoder to aid in manufacturability tasks where data can be limited. The model is evaluated across four manufacturability indicators, including subtractive machining time, additive manufacturing time, residual von Mises stress, and blade collisions during Laser Power Bed Fusion process. Results show that the VIRL-pretrained model exhibits improved generalizability with limited data and superior performance with larger datasets. Additionally, the LoRA (Low-rank adaptation) method is explored for deployment strategy, which balances probing and finetuning to achieve stable performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VIRL is a new way to prepare computer models for making things. It helps them learn from little data by using information about shapes and volumes. The model was tested on four different manufacturing tasks and showed it can make good predictions with small amounts of data or larger amounts of data. Another method called LoRA helped the model make even better predictions when there was some extra information available. This could be useful for making things like airplanes, cars, and machines. |
Keywords
» Artificial intelligence » Encoder » Lora » Low rank adaptation » Representation learning
