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

被引：0

作者：

Hasebe, Saki ^{[1
]}

Higuchi, Ryo ^{[1
]}

Yokozeki, Tomohiro ^{[1
]}

Takeda, Shin-ichi ^{[2
]}

机构：

[1] Univ Tokyo, Dept Aeronaut & Astronaut, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138656, Japan

[2] Japan Aerosp Explorat Agcy JAXA, Aviat Technol Directorate, 6-13-1 Osawa, Mitaka, Tokyo 1810015, Japan

来源：

DATA IN BRIEF | 2025年 / 60卷

关键词：

Thermoset CFRP; Laminates; Barely Visible Impact Damage; Compression after impact strength; Machine learning;

D O I：

10.1016/j.dib.2025.111509

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. (c) 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

引用

页数：12

共 14 条

[1]

[Anonymous], 2012, ASTM D7136

[2]

[Anonymous], 1996, JIS K 7089

[3]

Hasebe S., 2022, Mendeley Data v1

[4]

Hasebe S., 2025, **DATA OBJECT**, DOI 10.17632/8scdmfdcfb.3

[5]

Hasebe S., 2022, Mendeley Data

[6] Prediction of compression after impact strength from surface profile of low-velocity impact damaged CFRP laminates using machine learning [J].

Hasebe, Saki ;

Higuchi, Ryo ;

Yokozeki, Tomohiro ;

Takeda, Shin-ichi .

COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING, 2025, 189

[7] Multi-task learning application for predicting impact damage-related information using surface profiles of CFRP laminates [J].

Hasebe, Saki ;

Higuchi, Ryo ;

Yokozeki, Tomohiro ;

Takeda, Shin-ichi .

COMPOSITES SCIENCE AND TECHNOLOGY, 2023, 231

[8] Dataset for surface and internal damage after impact on CFRP laminates [J].

Hasebe, Saki ;

Higuchi, Ryo ;

Yokozeki, Tomohiro ;

Takeda, Shin-ichi .

DATA IN BRIEF, 2022, 43

[9] Internal low-velocity impact damage prediction in CFRP laminates using surface profiles and machine learning [J].

Hasebe, Saki ;

Higuchi, Ryo ;

Yokozeki, Tomohiro ;

Takeda, Shin-ichi .

COMPOSITES PART B-ENGINEERING, 2022, 237

[10] A Comprehensive Experimental and Computational Study on LVI induced Damage of Laminated Composites [J].

Lin, Shiyao ;

Ranatunga, Vipul ;

Waas, Anthony M. .

AIAA SCITECH 2021 FORUM, 2021,

← 1 2 →