Energy absorption prediction and optimization of corrugation-reinforced multicell square tubes based on machine learning. Issue 26 (26th October 2022)

• Record Type: Journal Article
• Authors: Li, Zhixiang; Ma, Wen; Zhu, Huifen; Deng, Gongxun; Hou, Lin; Xu, Ping; Yao, Shuguang
• Is Part Of: Mechanics of advanced materials and structures. Volume 29:Issue 26(2022)
• Page Start: 5511
• Page End: 5529
• Publication Date: 2022-10-26
• Subjects: Energy absorption -- corrugation-reinforced structure -- optimization -- deformation mode -- machine learning; Composite materials -- Mechanical properties -- Periodicals; Composite construction -- Periodicals; 620.118
• DOI: 10.1080/15376494.2021.1958032
• Languages: English
• ISSNs: 1537-6494

Abstract

Abstract: An energy absorbing tube combining multi-corner and multi-cell configurations was designed in this study. Machine learning was adopted to predict and optimize the crashworthiness of the proposed tube because it can handle both numerical and categorical responses. The results showed the increases in the considered geometric parameters caused the increases in the specific energy absorption and peak crushing force, while also made the unstable deformation mode prone to appear. Besides, with the help of machine learning, the accurate optimization results were obtained, in which the unstable deformation was removed. This work highlights the prospect of machine learning in structural optimizations.