碳纤维增强塑料层压板冲击试验压缩数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-03-22 DOI:10.1016/j.dib.2025.111509

Saki Hasebe , Ryo Higuchi , Tomohiro Yokozeki , Shin-ichi Takeda

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引用次数: 0

摘要

该数据集涵盖了从冲击后压缩（CAI）测试中获得的数据。在CAI测试之前，进行了各种实验条件（铺层、冲击器形状、冲击能量）下的低速冲击（LVI）测试，模拟各种异物对实际结构的冲击。CAI试验采用LVI试验所用试件和未损伤试件计算强度折减率。该数据集包括测试条件列表以及原始和处理过的数据：2. LVI测试条件；3.试样尺寸；3 . CAI测试后试件外观；4 .测试过程中从数据记录仪获得的原始数据；CAI的力量。该数据集的创建是为了寻求一种方法来预测CAI强度使用CFRP试件的损伤信息和实验条件。这些数据对研究碳纤维布的冲击行为或残余特性以及人工智能的研究人员和工程师有一定的帮助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Dataset of compression after impact testing on carbon fiber reinforced plastic laminates

This dataset covers the data obtained from the compression after impact (CAI) tests. Before the CAI tests, the low-velocity impact (LVI) testing was conducted under various experimental conditions (layup, impactor shape, and impact energy) to simulate various foreign object impacts on actual structures. For the CAI tests, both the specimens used in the LVI tests and undamaged specimens were utilized to calculate the strength reduction rate. This dataset includes a test condition list and raw and processed data: 1. LVI test conditions, 2. Specimen size, 3. Specimen appearance after the CAI tests, 4. Raw data obtained from the data logger during the testing, and 5. CAI strength. This dataset is created to seek a way to predict CAI strength using information on damage in CFRP specimens and the experimental condition. The data are helpful for researchers and engineers who are involved in the impact behavior or residual characteristics of CFRP and artificial intelligence.

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.