Predictive Soft Modeling of Turning Parameters Using Artificial Neural Network-Reference-Cited by-同舟云学术

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Predictive Soft Modeling of Turning Parameters Using Artificial Neural Network

Published:2021 Issue: Volume: Page:189-196
ISSN:2195-4356
Container-title:Lecture Notes in Mechanical Engineering
language:
Short-container-title:

Author:

Gupta N.,Walia R. S.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-3033-0_17

Reference15 articles.

1. Gupta, N., Agrawal, A. K., & Walia, R. S. (2019, May). Soft modeling approach in predicting surface roughness, temperature, cutting forces in hard turning process using artificial neural network: An empirical study. In International Conference on Information, Communication and Computing Technology (pp. 206–215). Singapore: Springer.

2. Davim, J. P. (2009). Machining composite materials. Wiley-ISTE.

3. Gupta, M., & Kumar, S. (2015). Investigation of surface roughness and MRR for turning of UD-GFRP using PCA and Taguchi method. Engineering Science and Technology, an International Journal, 18(1), 70–81.

4. Sangwan, K. S., Saxena, S., & Kant, G. (2015). Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia Cirp, 29, 305–310.

5. Karabulut, Ş. (2015). Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi meth od. Measurement, 66, 139–149.

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