聚酰亚胺
材料科学
润滑
复合材料
图层(电子)
摩擦学
聚四氟乙烯
韧性
仿生学
光致聚合物
聚合物
聚合
计算机科学
人工智能
作者
Xinle Yao,Yuxiong Guo,Yu Gao,Khan Rajib Hossain,Zhongying Ji,Zhibin Lu,Xiaolong Wang,Qihua Wang,Feng Zhou
标识
DOI:10.1016/j.triboint.2023.108972
摘要
Surface patterning has been widely utilized in structure enhancement, bionics, and surface lubrication. Drawing inspiration from the layer-by-layer forming principle of additive manufacturing and the Yin-Yang theory of traditional Chinese culture, we herein tailor patterned surfaces with various stripes of width using photosensitive polyimide and photosensitive polyimide-polytetrafluoroethylene composites that exhibit tough and lubricated properties by changing inks. To further optimize the self-lubricating surfaces, we present an accurate screening of the tribological data using machine learning (ML) and the optimized surfaces demonstrated the exceptional comprehensive properties. The combination of ML design and vat photopolymerization 3D printing is believed to enhance toughness and lubrication of surfaces, has the potential to the applications of the mechanical engineering, space equipment, and automobile manufacturing.
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