Modeling Electro-Erosion Wear of Cryogenic Treated Electrodes of Mold Steels Using Machine Learning Algorithms-Reference-Cited by-同舟云学术

Modeling Electro-Erosion Wear of Cryogenic Treated Electrodes of Mold Steels Using Machine Learning Algorithms

Published:2023-10-02 Issue: Volume: Page:15-26
ISSN:2195-4356
Container-title:Lecture Notes in Mechanical Engineering
language:
Short-container-title:

Author:

Cetin Abdurrahman^ORCID,Atali Gökhan^ORCID,Erden Caner^ORCID,Ozkan Sinan Serdar^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-6062-0_3

Reference11 articles.

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2. Ramaswamy, G.A., Krishna, A., Gautham, M., Sudharshan, S.S., Gokulachandran, J.: Optimisation and prediction of machining parameters in EDM for Al-ZrO2 using soft computing techniques with Taguchi method. IJPMB 11(6), 864 (2021). https://doi.org/10.1504/IJPMB.2021.118323

3. Sarıkaya, M., Yılmaz, V.: Optimization and predictive modeling using S/N, RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel. Neural Comput. Appl. 30(5), 1503–1517 (2018). https://doi.org/10.1007/s00521-016-2775-9

4. Balasubramaniam, V., Baskar, N., Narayanan, C.S.: Optimization of electrical discharge machining parameters using artificial neural network with different electrodes. In: 5th International & 26th All India Manufacturing Technology, Design and Research Conference (2014)

5. Ong, P., Chong, C.H., bin Rahim, M.Z., Lee, W.K., Sia, C.K., bin Ahmad, M.A.H.: Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond. J. Intell. Manuf. 31(1), 227–247 (2020). https://doi.org/10.1007/s10845-018-1443-6

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