Hasebe Saki, Higuchi Ryo, Yokozeki Tomohiro, Takeda Shin-Ichi
Department of Aeronautics and Astronautics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan.
Aeronautical Technology Directorate, Japan Aerospace Exploration Agency (JAXA), 6-13-1 Osawa, Mitaka-shi, Tokyo 181-0015, Japan.
Data Brief. 2022 Jul 10;43:108462. doi: 10.1016/j.dib.2022.108462. eCollection 2022 Aug.
Various foreign objects can collide with CFRP structures, such as CFRP aircraft. Once something impacts with CFRP laminates, both surface damage and internal damage can occur. Even if the external damage is such invisible as called barely visible impact damage, there are matrix cracks or delamination that are the main cause of compressive strength reduction, so it is difficult to find the relationship between external and internal damage on CFRP laminates. This dataset is prepared for predicting impact information only from surface damage profiles using Machine Learning (Hasebe et al., 2022). It includes three data, surface damage image (png), surface depth contour image(png), and internal damage image after ultrasound C-scanning (jpg) after low-velocity impact testing under various impact conditions. The data are helpful for researchers and engineers who deal with the impact behavior of CFRP or data science.
各种异物可能会与碳纤维增强塑料（CFRP）结构发生碰撞，比如CFRP飞机。一旦有物体撞击CFRP层压板，就可能会出现表面损伤和内部损伤。即使外部损伤是那种被称为几乎不可见的冲击损伤，肉眼难以察觉，但仍会存在基体裂纹或分层，这是导致抗压强度降低的主要原因，所以很难找到CFRP层压板外部损伤与内部损伤之间的关系。这个数据集是为了仅通过机器学习，利用表面损伤轮廓来预测冲击信息而准备的（长谷部等人，2022年）。它包含三个数据，分别是表面损伤图像（png格式）、表面深度轮廓图像（png格式），以及在各种冲击条件下进行低速冲击试验后经超声C扫描得到的内部损伤图像（jpg格式）。这些数据对研究CFRP冲击行为的研究人员和工程师或数据科学领域的人员很有帮助。
