Carbon fiber reinforced polymer defect detection using magnetic induction tomography method

Abstract

Carbon Fiber Reinforced Polymer (CFRP) is widely used in aerospace, military and other fields due to its excellent performance. In order to meet the requirements of rapid detection, location and visualization of defects in carbon fiber composite reinforced polymer. The defect detection method of carbon fiber reinforced polymer based on magnetic induction tomography (MIT) was investigated. MIT of four-layer unidirectional carbon fiber reinforced polymer laminates model was established by COMSOL Multiphysics, a planar electromagnetic sensor array for CFRP laminates was designed, the sensitivity matrix was calculated based on the reciprocity theorem and correction factor P was introduced to eliminate the influence of negative sensitivity on image reconstruction, then Tikhonov regularization algorithm and Landweber algorithm were used for image reconstruction of CFRP laminates defects. Thus, the results show that the designed planar sensor array can effectively detect the defects of CFRP laminates, at the same time, image reconstruction based on improved sensitivity has better effect on defect detection, and the shape and location of defects are more accurate; It shows that MIT is feasible for defect detection of carbon fiber reinforced polymer.