Research On Damage Characterization Methods For Composite Materials Based On Acoustic Emission And DIC

Fiber reinforced composites have been widely used in composite structures in aerospace,transportation planning and other fields due to their excellent properties such as light weight,high strength,easy molding,and strong designability.In practical applications,it is often necessary to make holes in the structure to meet the requirements of structural join and window.However,holes will destroy the continuity of the structure and cause stress concentration or local damage,thus reducing the mechanical properties of composite materials,seriously restricting its application in engineering.Based on this,studying the damage evolution process and failure modes of carbon fiber composite materials with openings is of great significance for promoting the large-scale application of carbon fiber composite materials.This article takes carbon fiber epoxy resin based CFRP laminates as the research object,and uses DIC and AE technology to monitor the entire process of unidirectional tensile testing to study the damage evolution process of different pore diameters and structures without pores under tensile load.Firstly,research on the DIC quantification method for tensile damage of carbon fiber composite materials.By combining DIC and acoustic emission technology,the entire process of unidirectional tensile testing of CFRP laminates with prefabricated holes of different diameters and unopened holes was monitored.The strain field cloud maps of each specimen under different loads were analyzed,and it was found that the larger strain points were closely related to the material damage state.On the basis of full field experimental data statistics and DIC cloud image analysis,the normalized mean deviation(NAD)and spatial correlation coefficient(SCC)of strain field statistics are proposed as key parameters to describe the degree of damage and spatial distribution.As the diameter of the opening increases,larger strain points will quickly gather at the pre cracking position and tend to stabilize,reducing the specimen’s ability to resist external loads.Secondly,damage pattern recognition of carbon fiber composite materials based on Pearson-PCA-FCM clustering method.For the analysis of tensile acoustic emission damage signals of CFRP laminates with different pre fabricated hole diameters and unopened holes,the Pearson correlation coefficient algorithm is used to screen features,while the PCA algorithm is used for data dimensionality reduction.The FCM clustering method is used to classify the damage in the optimized new dataset.Using this method to solve the problem of large data discreteness in different damage mechanisms of composite materials,a clustering method based on Pearson-PCA-FCM is established to achieve pattern recognition of different damages in composite materials.The presence of prefabricated holes is more likely to cause fiber fracture damage to the material,and the fiber fracture damage will strengthen with the increase of the opening diameter.Finally,the residual bearing capacity prediction of CFRP laminates based on BP neural network.Using the RRelif F algorithm to evaluate the weight of characteristic parameters that characterize residual bearing capacity,such as acoustic emission parameters and partial cumulative features,select the cumulative features with higher weight coefficients and fuse them with the characteristics of DIC strain cumulative parameters and associate them with the corresponding loads,establish a BP neural network-based prediction model for residual bearing capacity of laminated plates,and achieve the prediction of residual bearing capacity of laminated plates.