Application of artificial intelligence techniques in incremental forming: a state-of-the-art review
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DOI: 10.1007/s10845-021-01868-y
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- Hietam Elhoone & Tianyang Zhang & Mohd Anwar & Salil Desai, 2020. "Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2841-2861, May.
- Shubham Vaishnav & Ankit Agarwal & K. A. Desai, 2020. "Machine learning-based instantaneous cutting force model for end milling operation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1353-1366, August.
- Christoph Hartmann & Daniel Opritescu & Wolfram Volk, 2019. "An artificial neural network approach for tool path generation in incremental sheet metal free-forming," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 757-770, February.
- Ali Alsamhan & Adham E Ragab & Abdulmajeed Dabwan & Mustafa M Nasr & Lotfi Hidri, 2019. "Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.
- Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
- Juncheng Wang & Bin Zou & Mingfang Liu & Yishang Li & Hongjian Ding & Kai Xue, 2021. "Milling force prediction model based on transfer learning and neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 947-956, April.
- Ivanna Baturynska & Kristian Martinsen, 2021. "Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 179-200, January.
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Keywords
Incremental forming; Artificial intelligence; Artificial neural network;All these keywords.
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