Comparative Study of Feed-Forward and Recurrent Neural Networks in Modeling of Electron Beam Welding-Reference-Cited by-同舟云学术

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Comparative Study of Feed-Forward and Recurrent Neural Networks in Modeling of Electron Beam Welding

Published:2019-10-17 Issue: Volume: Page:521-531
ISSN:2522-5022
Container-title:Lecture Notes on Multidisciplinary Industrial Engineering
language:
Short-container-title:

Author:

Jaypuria Sanjib^ORCID,Gupta Santosh Kumar^ORCID,Pratihar Dilip Kumar^ORCID

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-32-9433-2_45

Reference15 articles.

1. Islam, M., Buijk, A., Rais-Rohani, M., Motoyama, K.: Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique. Adv. Eng. Softw. 79, 127–136 (2015)

2. Jaypuria, S., Doshi, N., Pratihar, D.K.: Effects of welding parameters on mechanical properties in electron beam welded CuCrZr alloy plates. IOP Conf. Series Mater. Sci. Eng. 338(1), 012013 (2018)

3. Schultz, H.: Electron Beam Welding. Abington Publishing, England (2004)

4. Zhao, D., Ren, D., Zhao, K., Pan, S., Guo, X.: Effect of welding parameters on tensile strength of ultrasonic spot-welded joints of aluminum to steel—by experimentation and artificial neural network. J. Manuf. Process. 30, 63–74 (2017)

5. Pratihar, D.K.: Soft Computing: Fundamentals and Applications. Narosa Publishing, New Delhi (2014)

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