Work

Prediction of Fatigue Damage Evolution in 3D-Printed CFRP Based on Ultrasonic Testing and LSTM

Year: 2026

Type: article

Abstract: To address the prediction of fatigue damage for 3D-printed Carbon Fiber Reinforced Polymer (CFRP), this study used 3D-printing technology to fabricate CFRP specimens. Through multi-stage fatigue testing, samples with varying porosity levels were obtained. Based on porosity test results and ultrasonic attenuation coefficient measurements of specimens under different fatigue cycle counts, a quantitative relationship model was established between the porosity and ultrasonic attenuation coefficient of 3D-printed CFRP. According to the porosity and fatigue-loading cycles obtained from tests, the Time-series Generative Adversarial Network (TimeGAN) algorithm was employed for data augmentation to meet the requirements for neural-network training. Subsequently, the Long Short-Term Memory (LSTM) neural network was utilized to predict the fatigue damage evolution of 3D-printed CFRP specimens. Research findings indicate that by integrating the established relationship between porosity and ultrasonic attenuation coefficient, non-destructive testing of material fatigue damage evolution based on ultrasonic attenuation coefficient can be achieved.

Source: Applied Sciences

DOI: 10.3390/app16021139

Authors: Erzhuo Li, Sha Xu, Hongqing Wan, Hao Chen, Yali Yang, Yongfang Li

Institution: Shanghai University of Engineering Science

Language: English

Topic: Mechanical Behavior of Composites

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896