EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 430 (2023) E3S Web Conf., 430 (2023) 01252 References
Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01252
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202343001252
Published online 06 October 2023
  1. Russell, S. and Norvig, P., 2002. Artificial intelligence: a modern approach. [Google Scholar]
  2. Nilsson, N.J., 2009. The quest for artificial intelligence. Cambridge University Press. [Google Scholar]
  3. Minsky, M., 1961. Steps toward artificial intelligence. Proceedings of the IRE, 49(1), pp.8-30. [CrossRef] [Google Scholar]
  4. Negnevitsky, M., 2005. Artificial intelligence: a guide to intelligent systems. Pearson education. [Google Scholar]
  5. Cohen, P.R. and Feigenbaum, E.A. eds., 2014. The Handbook of Artificial Intelligence: Volume 3 (Vol. 3). Butterworth-Heinemann. [Google Scholar]
  6. Jarvis, R.A., 1983. A perspective on range finding techniques for computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2), pp.122-139. [CrossRef] [Google Scholar]
  7. Voulodimos, A., Doulamis, N., Doulamis, A. and Protopapadakis, E., 2018. Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 2018. [PubMed] [Google Scholar]
  8. Jähne, B., Haussecker, H. and Geissler, P. eds., 1999. Handbook of computer vision and applications (Vol. 2, pp. 423-450). New York: Academic press. [Google Scholar]
  9. Faugeras, O. and Faugeras, O.A., 1993. Three-dimensional computer vision: a geometric viewpoint. MIT press. [Google Scholar]
  10. Du, Y., Mukherjee, T. & DebRoy, T. Conditions for void formation in friction stir welding from machine learning. npj Comput Mater 5, 68 (2019). https://doi.org/10.1038/s41524-019-0207-y [CrossRef] [Google Scholar]
  11. Du, Y., Mukherjee, T., Mitra, P. and DebRoy, T., 2020. Machine learning based hierarchy of causative variables for tool failure in friction stir welding. Acta Materialia, 192, pp.67-77. [CrossRef] [Google Scholar]
  12. Mishra, A. and Patti, A., 2021. Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints. [Google Scholar]
  13. Mishra, A. and Pathak, T., 2020. Estimation of Grain Size Distribution of Friction Stir Welded Joint by using Machine Learning Approach. [Google Scholar]
  14. Mishra, R.S. and Ma, Z.Y., 2005. Friction stir welding and processing. Materials science and engineering: R: reports, 50(1-2), pp.1-78. [CrossRef] [Google Scholar]
  15. Rai, R., De, A., Bhadeshia, H.K.D.H. and DebRoy, T., 2011. friction stir welding tools. Science and Technology of welding and Joining, 16(4), pp.325-342. [CrossRef] [Google Scholar]
  16. Lohwasser, D. and Chen, Z. eds., 2009. Friction stir welding: From basics to applications. Elsevier. [Google Scholar]
  17. Thomas, W.M., Johnson, K.I. and Wiesner, C.S., 2003. Friction stir welding recent developments in tool and process technologies. Advanced engineering materials, 5(7), pp.485-490. [CrossRef] [Google Scholar]
  18. Gite, R.A., Loharkar, P.K. and Shimpi, R., 2019. Friction stir welding parameters and application: A review. Materials Today: Proceedings, 19, pp.361-365. [Google Scholar]
  19. Mishra, A., 2018. Friction stir welding of dissimilar metal: a review. Available at SSRN 3104223. [Google Scholar]
  20. Mishra, A. and Dixit, D., 2018. Friction Stir Welding of Aerospace Alloys. Journal of Mechanical Engineering, 48(1), pp.37-46. [Google Scholar]
  21. Hartl, R.; Bachmann, A.; Habedank, J.B.; Semm, T.; Zaeh, M.F. Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks. Metals 2021, 11, 535. https://doi.org/10.3390/met11040535 [CrossRef] [Google Scholar]
  22. R. Hartl, J. Landgraf, J. Spahl, A. Bachmann, and M. F. Zaeh “Automated visual inspection of friction stir welds: a deep learning approach”, Proc. SPIE 11059, Multimodal Sensing: Technologies and Applications, 1105909 (21 June 2019); https://doi.org/10.1117/12.2525947 [Google Scholar]
  23. Sakthivel, T. and Mukhopadhyay, J., 2007. Microstructure and mechanical properties of friction stir welded copper. Journal of Materials Science, 42(19), pp.8126-8129. [CrossRef] [Google Scholar]
  24. Lee, W.B. and Jung, S.B., 2004. The joint properties of copper by friction stir welding. Materials Letters, 58(6), pp.1041-1046. [CrossRef] [Google Scholar]
  25. Mironov, S., Inagaki, K., Sato, Y.S. and Kokawa, H., 2015. Microstructural evolution of pure copper during friction-stir welding. Philosophical Magazine, 95(4), pp.367-381. [CrossRef] [Google Scholar]
  26. Savolainen, K., 2012. Friction stir welding of copper and microstructure and properties of the welds. [Google Scholar]
  27. Sun, Y.F. and Fujii, H., 2010. Investigation of the welding parameter dependent microstructure and mechanical properties of friction stir welded pure copper. Materials Science and Engineering: A, 527(26), pp.6879-6886. [CrossRef] [Google Scholar]
  28. Shen, J.J., Liu, H.J. and Cui, F., 2010. Effect of welding speed on microstructure and mechanical properties of friction stir welded copper. Materials & Design, 31(8), pp.3937-3942. [CrossRef] [Google Scholar]
  29. Kumar, A. and Raju, L.S., 2012. Influence of tool pin profiles on friction stir welding of copper. Materials and Manufacturing Processes, 27(12), pp.1414-1418. [CrossRef] [Google Scholar]
  30. Xie, G.M., Ma, Z.Y. and Geng, L., 2007. Development of a fine-grained microstructure and the properties of a nugget zone in friction stir welded pure copper. Scripta Materialia, 57(2), pp.73-76. [CrossRef] [Google Scholar]
  31. Liu, H.J., Shen, J.J., Huang, Y.X., Kuang, L.Y., Liu, C. and Li, C., 2009. Effect of tool rotation rate on microstructure and mechanical properties of friction stir welded copper. Science and Technology of welding and Joining, 14(6), pp.577-583. [CrossRef] [Google Scholar]
  32. Sun, Y.F. and Fujii, H., 2011. The effect of SiC particles on the microstructure and mechanical properties of friction stir welded pure copper joints. Materials Science and Engineering: A, 528(16-17), pp.5470-5475. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.