Influence analysis of key parameters in laser scanning thermography nondestructive testing

Zhengwei, 杨正伟 Yang; Xingyu, 谢星宇 Xie; Yin, 李 胤 Li; Wei, 张炜 Zhang; Gan, 田 干 Tian

doi:10.3788/irla201948.1105008

Cited by 2 publications

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“…In laser thermography methods, key parameters, such as laser power and scanning speed, have an important influence on the detection quality [29,30]. In order to guide the selection of experimental parameters of laser thermography, based on finite element method, a simulation model with a vertical surface crack is established.…”

Section: Simulation Analysis Of Factors Affecting Detection Effectmentioning

confidence: 99%

Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography

et al. 2022

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As a revolutionary new technique, laser-engineered net shaping (LENS) is a layer additive manufacturing process that enables accurate, rapid and automatic repair of industrial component damage. In the laser repair (LR) process or in service, surface cracks can appear, which have a detrimental effect on the repair quality and the mechanical performance; therefore, the surface crack detection of repaired components has attracted much attention. Laser spot thermography is an important nondestructive testing method with the advantages of non-contact, full-field and high precision, which shows great potential in the crack detection of repaired components. The selection of thermographic process parameters and the optimization of thermal image processing algorithms are key to the success of the nondestructive detection. In this paper, the influence of material properties and thermographic process parameters on the surface temperature gradient is studied based on the simulation of laser spot thermal excitation, and the selection windows of thermographic process parameters for iron-based and nickel-based alloys are obtained, which is applied to the surface crack detection of repaired components. To improve the computational efficiency of thermal images, the Prewitt edge detection algorithm is used in the thermal image processing, which realized fast extraction of cracks with a high signal-to-noise ratio (SNR), and the detection sensitivity of crack width can reach 10 μm. To further study the influence of surface roughness on the thermographic detection, repair layers with and without polishing process are characterized, which show that the Prewitt edge detection algorithm is well applicable to crack detection on surfaces with different roughness level.

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Section: Simulation Analysis Of Factors Affecting Detection Effectmentioning

confidence: 99%