Quantitative Detection for Fatigue Natural Crack in Aero-Aluminum Alloy Based on Pulsed Eddy Current Technique

Abstract

Aero-space aluminum alloys, as vital materials in aerospace engineering, find extensive application in various aerospace components. However, prolonged usage often leads to the emergence of fatigue natural cracks, posing significant safety risks. Therefore, research on accurate quantitative detection techniques for the cracks in aerospace-aluminum alloys is of vital importance. Firstly, based on the three-points bending experimental model, this paper prepared the fatigue natural crack specimen, and the depth of the natural crack is calibrated. Then, given the complexity of geometric characteristics inherent in natural cracks, the pulsed eddy current signal under the different natural crack depth is acquired and analyzed using an experimental study. Finally, to better exhibit the non-linearity between PEC signal and crack depth, a GA-based BPNN algorithm is proposed. The Latin Hypercube method is considered to optimize the population distribution in the genetic algorithm. The results indicate that the characterization accuracy reaches 2.19% for the natural crack.

Keywords

• pulsed eddy current technique
• fatigue natural crack
• aero-aluminum alloy
• BP neural network
• Latin Hypercube