Research On ACFM Based Defect Intelligent Recognition And Visualization Technique

The dissertation is focused on the technology of defect intelligent recognition and visualization based on the alternating current field measurement (ACFM), including the FEM simulating analysis, the intelligent recognition and visualization of defect, the design and optimization for probe, electromagnetic signals processing and the development of virtual prototype system and so on, which is supported by'863'Youth Fund of the High Technology Research and Development Program of China (No. 2002AA616060). The main works are summarized as follows:1 Study of the Theory and Numerical Model of ACFMBased on the principle of ACFM and the theory of numerical calculation, the magnetic vector A and the scalar potential function ? are introduced, and the theory model of ACFM defect recognition is presented. To analyze and calculate the electromagnetic field distribution around the defect in that theory model, the finite element analysis (FEA) method is inducted, and three FEA models are built for different subjects and aims. The induction model is used to simulate the induced electromagnetic field distribution in ACFM, which provides the theoretical foundation to design and optimize the probe. The calculation model is used to analyze and calculate the distribution of the magnetic flux density above the defect in the state of stable measurement. And in the circumstance of moving scan or the measured defect is bigger than the area of the induced field, the probe moving simulation model will be used to analyze and calculate the distribution of the magnetic field. These three specific models mentioned above make the workload of building model and analysis of ACFM electromagnetic field distribution decreasing effectively, and provide the foundation of the following study.2 Research on the Algorithm of Defect Intelligent Recognition and VisualizationConsidering the deficiency of involving man made error, difficulties of real-time recognition and low degree of automation and intelligence in the field of defects recognition, a deep research on the technique of defect intelligent recognition in ACFM is performed. As the basis of intelligent defect recognition, the defect database is built by combining the artificial defects producing and simulating. The characteristic vectors of defect signals are determined to reveal the defect information and simplify the algorithm by signal processing and feature extraction. Analyzing the shortcoming of defect discrimination methods in ACFM, a phase involved direct method used in defects real-time discrimination is presented on the basis of cross-correlation detection method. Based on the three rules describing the relationship between the magnetic field distribution and the size of defects, a interpolation algorithm of quantitative recognition of defects for ACFM is proposed, which presents a new method of defect real-time recognition in engineering. Considering the complex nonlinear relationship between the size of the defect and the distribution of the characteristic signals, the nonlinear neural network is introduced, and the BP neural network model and the generalized regression neural network (GRNN) model for defect recognizing in ACFM are built. Based on the rotational induced magnetic field, a method for calculating the direction of the defect in ACFM is proposed, which makes the foundation of defect shape visualization.The relationship between the distribution of the magnetic field signals and the defect shape is analyzed by lots of finite element numerical simulating experiments of arbitrary shape defects. On the basis of finite element segmentation method, the inversion algorithm of the section shape and surface shape of the defect and the reconstruction method of three dimensional profile of the defect are proposed. These algorithms and method are verified by simulation experiments, and the results show that the precision is higher than 85 percent. Combined the inversion algorithm of the defect shape with the finite element simulation technique, the intelligent process of defect shape visualization is designed by the optimization method.3. Development of ACFM Intelligent and Visual Recognition SystemConsidering the characteristic of the magnetic field signals in ACFM, the relationship between the structure of the inducer and the induced magnetic field is discussed by parameterized induced simulation model. On the basis of the principle of rotational magnetic field, a double U-shaped orthogonal inducer for ACFM is presented, which could clear up the unfavorable influence of the defect direction on the sensitivity of measurement. A one dimensional defect intelligent and visual recognition probe of ACFM is developed with one dimension array detecting coils, which provides enough magnetic information for intelligent defect recognition and visualization. Considering the weakness of the signals and disturbance of noises, by designing the signal producing and processing current circuits, the primary signal process is realized, which makes the foundation for A/D conversion and digital signal process.Using an A/D data acquisition card, by the LabVIEW language, the signal acquisition and conversion module is developed. And aiming at simplifying current circuits, and decreasing cost, parts of current circuits are realized by computer software, and the digital signal process software module is designed. On the basis of the principle of orthogonal lock-in amplification, a cross-correlation phase detector is developed, by which both the phase and amplitude of signal are measured. Due to the advantage of MATLAB software in the field of numerical calculation and analysis, the defect intelligent and visual recognition module is produced by the method of mixed language programming. Finally, the ACFM defect intelligent and visual recognition software system is built by combining these modules mentioned above, which can be used to realize the real-time defect discrimination, intelligent quantifying recognition, and visual description of defect shape.The ACFM defect intelligent and visual recognition system is tested by experiments of measuring artificial defects in the field of function precision and performance under some special circumstances. The results show that the quantifying precision of this system is about 90 percent, and the shape inversely calculated is similar to the real shape of the defect, and the defect under water can be also recognized by this system intelligently and visually, the precision about 85 percent.4 Design of Virtual Prototype System of ACFMBased on the principle of ACFM, researching on the forward and inverse problems such as design and calculation of the FEA parametrical model, simulation results process and feature extraction, maintenance of the defect characteristic database, and defect quantifying recognition and visual inversion and so on, a virtual prototype system for defect intelligent recognition and visualization based on ACFM is built by the numerical simulation technique by C++ BUILDER language. Aiming to adjust the parameters of virtual prototype and test the function of measurement system, an interface subsystem is designed to link the measurement system with the virtual prototype. In addition, the virtual prototype could be used as a post-processing module for signals acquired by the probe of measurement system.