Study On Non-destructive Testing Method For Slit Defects Of Ferromagnetic Materials Based On PEC

Magnetic materials are widely used in large-scale machinery and equipment,chemical tanks and oil pipelines,and so on.If the cracks can't been detected,it will lead to major safety accidents.At present,there are many researches focus on detection techniques for the non-magnetic materials,while the research for the magnetic materials is quite few;more and more attentions are paid on magnetic flux leakage,magnetic powder and far field eddy current technology,while the research of pulsed Eddy current technology is very few;There are many researches on corrosion crack and thickness detection of magnetic materials pipelines,but there is very little detection on the natural crack of the plate specimen;There are more forward studies on defect detection,but less research on inverse problem of defects;The algorithm of defect inversion is more traditional,and the intelligent algorithm is seldom considered in inverse problem.Therefore,it is necessary to study the flaw detection by the pulse eddy current technology for the magnetic material,and it is necessary to apply the intelligent algorithm to inverse the defect.This thesis focus on:(1)Study on the principle and numerical simulation of slit defect detection of pulsed eddy current magnetic materials.Firstly,the principle of magnetic material defect detection is studied,which provides theoretical basis for simulation and experiment.Then the finite element model of defect detection is established,and the influence of relative permeability,conductivity and lift-off on the magnetic flux density is studied.Followed that the influences of the depth and width of the defects on the magnetic flux density are investigated.Based on the investigation,the relationship between the flaw width/ depth and the magnetic flux density are obtained.(2)Experimental study on slit defect detection for the magnetic material.Firstly,the software and hardware of the test platform for defect detection in magnetic material are built;Then the experimental research on the depth and width defects is carried out;Next,based on the experiment data,the comparison of experiment and simulated data is conducted,on the one hand the correctness of simulation model is proved,on the other hand,it can provide sample data for defect inversion algorithm research.(3)Research on defect inversion algorithm based on BP neural network.Based on neural network,the inverse algorithm for the defect in magnetic material is studied,andthe inversion results with BP neural networks,radial basis neural networks,generalized regression network and probabilistic neural network for the defect are compared and the neural network algorithm is compared for defect inversion,Finally BP neural network is selected as the defect inversion algorithm.Then the BP neural network is optimized by training value and genetic algorithm to improve the accuracy of inversion error of BP neural network.In the end,the traditional inversion algorithm is compared with BP neural network to show the superiority of the neural network.