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利用光声效应探测亚表面层中的缺陷
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作者 李增发 张春平 +1 位作者 范文锦 颜彩繁 《四川激光》 1981年第A02期82-82,共1页
本文简介了激光光声探测亚表面层缺陷的原理和方法,并利用我们的光声实验装置测量了样品中存在的缺陷和范性形变。
关键词 表面层缺陷 光声效应 声探测 范性形变 实验装置
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Analysis and Control of Surface Delamination Defects During Milling of Orthogonal Aramid Fiber‑Reinforced Composites Laminates 被引量:2
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作者 SHI Zhenyu DUAN Ningmin +1 位作者 LI Xin WANG Zhaohui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第3期353-359,共7页
The aramid fiber-reinforced composites(AFRC)can increase the durability of corresponding applications such as aerospace,automobile and other large structural parts,due to the improvement in hardness,heat build-up,wear... The aramid fiber-reinforced composites(AFRC)can increase the durability of corresponding applications such as aerospace,automobile and other large structural parts,due to the improvement in hardness,heat build-up,wear properties and green environmental protection.However,because of its complex multiphase structure and unique heterogeneity and anisotropy,the poor compression fatigue resistance and the incident surface fibrillation are inevitable.To improve the assembly precision of AFRC,mechanical processing is necessary to meet the dimensional accuracy.This paper focuses on the influence of contour milling parameters on delamination defects during milling of AFRC laminates.A series of milling experiments are conducted and two different kinds of delamination defects including tearing delamination and uncut-off delamination are investigated.A computing method and model based on brittle fracture for the two different types of delamination are established.The results can be used for explaining the mechanism and regularity of delamination defects.The control strategy of delamination defects and evaluation method of finished surface integrity are further discussed.The results are meaningful to optimize cutting parameters,and provide a clear understanding of surface defects control. 展开更多
关键词 aramid fiber-reinforced composites(AFRC) milling process delamination defects surface control prediction model
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