Discussion on infrared stress measurements based on finite element analysis of transient heat conduction

• Published in: Mechanical Engineering Journal 2024, Vol.11(4), pp.23-00571-23-00571
• Main Authors: KOJIMA, Yuta, HIRAYAMA, Kenta, HARADA, Yoshihisa, MURAMATSU, Mayu
• Format: Article
• Language: English
• Subjects: Carbon fiber reinforced plastic; Finite element method; Infrared stress measurement; Non-destructive testing; Transient heat conduction analysis
• ISSN: 2187-9745; 2187-9745
• DOI: 10.1299/mej.23-00571

Carbon Fiber Reinforced Plastic (CFRP) is a composite material consisting of a resin matrix and carbon fiber reinforcement. The material is also commonly used in a prepreg laminate, a unidirectional reinforcing material with high strength and stiffness in one direction. Damage to laminates is highly complex, including delamination, fiber fracture, and base matrix cracking, requiring highly efficient and accurate non-destructive testing. Infrared stress measurement is an example of a non-destructive testing method for CFRP structures. The infrared stress measurement is based on Kelvin’s theory to convert the surface temperature fluctuation under cyclic loading to the distributions of sum of principal stress on surface (DSPSS), so it may result in a stress distribution that differs from the actual distribution due to transient heat conduction. It is necessary to consider structural analysis and transient heat conduction in the numerical analysis to reproduce DSPSS obtained by infrared stress measurement. This study performs a finite element analysis with transient heat conduction on simple shaped CFRP specimens to reproduce the trend of DSPSS obtained by infrared stress measurement. Firstly, DSPSS generated by the forced displacement of a CFRP specimen is converted to a temperature distribution using Kelvin’s thermoelastic theory. Finally, a transient heat conduction analysis is performed, and the distribution trend is discussed using the obtained temperature distribution as the initial value. A sheet of Teflon is inserted into the CFRP specimen as a defect, assuming foreign matter contamination during the manufacturing process. Previous study predicts the internal defect information by a machine learning model using the DSPSS from numerical analysis. There is a potential for a high-accuracy defect prediction using DSPSS obtained by the infrared stress measurement if DSPSS obtained by the infrared stress measurement and the numerical analysis have similar tendencies.