| Carbon fiber reinforced composite materials are widely used in fields such as automobiles,aerospace,and ocean engineering due to their high modulus,light weight,and ease of processing.However,due to immature processing technology and impact during service,internal defects may exist,which seriously affects the material’s performance and poses safety hazards.Therefore,effectively detecting internal defects is of great significance for its safe service and reducing safety hazards.This academic dissertation focuses on the detection of internal defects in carbon fiber reinforced composite materials using laser speckle shearing interferometry,with a focus on key technologies such as defect localization detection,defect recognition,and out of plane displacement measurement.The main content is as follows:1.Research on the localization and detection of internal layered defects based on laser speckle shearing interference.By combining the detection mechanism of laser speckle shearing interference technology with the constitutive relationship of material bending deformation,a mathematical model for measuring the depth of internal defects in the research object is established.Based on the material mechanical properties,the depth of defects is located;By simulating and studying the effects of defect parameters,loading conditions,and shear amount on speckle shear interference fringes,parameter references are provided for detection;The laser speckle shear interference detection platform was used to locate and detect the layered defects of prefabricated carbon fiber reinforced composites.Within a certain range of defect attributes,the depth positioning error of defects was less than 15%,which basically met the detection requirements.2.Due to the significant differences in the characteristics of defect wrapping phase maps under different loading conditions,traditional object detection cannot achieve defect recognition.Firstly,analyze the characteristics of the package phase diagram of defects under different loading conditions,and use the traditional object detection algorithm based on Harris corner detection to identify defects in the package phase diagram under vacuum loading;For the thermal loaded global micro deformation interference fringe,the traditional target detection cannot achieve effective recognition.Using the depth learning based YOLO target detection algorithm,Focal loss loss function is used to improve the imbalance of the number of positive and negative samples in the single stage target detection.By comparing the training results before and after the improvement,the convergence of the training is improved,The accuracy of the predicted results reached95%,which solved the binary identification of defects in the parcel phase diagram under different loading conditions.3.To achieve surface off plane displacement measurement of complex structures,the influence of different surfaces on off plane displacement measurement is studied.By analyzing the measuring principle of the speckle shearing method for the out of plane displacement of different surfaces,the influence of the angle between the deformation vector of the object surface and the optical axis on the out of plane displacement measurement is explored,and an out of plane displacement measurement method based on the surface structured light and speckle shearing is proposed to obtain the shape information of the object surface relative to the phase machine,and correct the out of plane displacement measured by the speckle shearing method.The feasibility of the method is verified by measuring the maximum off plane displacement of aluminum plates in different directions.The root mean square error of the measurement is less than 0.9um,which meets the actual measurement requirements. |
