Using GANs to predict milling stability from limited data

Author:

Rezaei Shahrbanoo,Cornelius Aaron,Karandikar Jaydeep,Schmitz Tony,Khojandi AnahitaORCID

Funder

Federal Agency/ DOE/EERE/Advanced Manufacturing Office

Division of Engineering Education and Centers

UT-Battelle

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference52 articles.

1. Akcay, S., Atapour-Abarghouei, A., Breckon, T. P. (2019). Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In 2019 international joint conference on neural networks (IJCNN) (pp. 1–8). IEEE.

2. Altintas, Y., & Budak, E. (1995). Analytical prediction of stability lobes in milling. CIRP Annals, 44(1), 357–362.

3. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. PMLR: In International conference on machine learning (pp. 214–223).

4. Bang, D., & Shim, H. (2018). Improved training of generative adversarial networks using representative features. In International conference on machine learning (pp. 433–442).

5. Bang, D., & Shim, H. (2021). Mggan: Solving mode collapse using manifold-guided training. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 2347–2356).

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