Neural networks with dimensionality reduction for efficient springback prediction in deep drawing of multi-material cylindrical cups-Reference-Cited by-同舟云学术

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Neural networks with dimensionality reduction for efficient springback prediction in deep drawing of multi-material cylindrical cups

Published:2023-02-25 Issue: Volume: Page:1-20
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Hou Chun Kit Jeffery¹,Behdinan Kamran¹

Affiliation:

1. Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2023.2183271

Reference54 articles.

1. Wear behaviour on the radius portion of a die in deep-drawing: Identification, localisation and evolution of the surface damage

2. Analytical Study and FEM Simulation of the Maximum Varying Blank Holder Force to Prevent Cracking on Cylindrical Cup Deep Drawing

3. Detailed experimental and numerical analysis of a cylindrical cup deep drawing: Pros and cons of using solid-shell elements

4. Deep drawing process: analysis and experiment

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. 2S-ML: A simulation-based classification and regression approach for drawability assessment in deep drawing;International Journal of Material Forming;2023-08-22

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