轻工机械2026，Vol.44Issue(4)：77-86,10.DOI:10.3969/j.issn.1005-2895.2026.04.010
基于1DCNN的碳纤维复材裂纹损伤涡流检测方法
Eddy Current Detection Method for Cracks and Damage in Carbon Fiber Composites Based on 1DCNN
摘要
Abstract
To address the difficulty of locating and quantifying subsurface damage in eddy current testing of carbon fiber composite materials,the research group proposed a method based on a 1-Dimensional Convolutional Neural Network(1DCNN)for eddy current testing.By constructing a benchmark dataset covering 9-dimensional variables,including scanning position and angle,and employing a multi-modal self-selection fusion technique to integrate 4 signal modalities,an end-to-end 1DCNN model from eddy current signals to defect parameters was established.Quantitative evaluation results show that this method achieves an Mean Absolute Error(MAE)of 0.36 mm for subsurface crack damage localization and 0.02 mm for depth quantification evaluation.This study provides a practical intelligent solution for the nondestructive testing of composite material structures.关键词
涡流检测/碳纤维复合材料/一维卷积神经网络/基准数据集/多模态自选择融合技术Key words
eddy current testing/CFRP(Carbon Fiber Reinforced Polymer)/1DCNN(1-D Convolutional Neural Network)/benchmark dataset/multi-modal self-selection fusion technique分类
机械制造引用本文复制引用
张鹏哲,应志平,王俊茹..基于1DCNN的碳纤维复材裂纹损伤涡流检测方法[J].轻工机械,2026,44(4):77-86,10.基金项目
国家自然科学基金项目(52305428) （52305428）
国家重点研发计划重点专项(SQ2023YFB3200093) （SQ2023YFB3200093）
国家重点研发计划项目(2023YFB3210900). （2023YFB3210900）
