Browsing by Author "Lin, Minxiao"

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    3D-printed thermoplastic composite fasteners for single lap joint reinforcement
    (Elsevier, 2021-12-10) Li, Wenhao; Guo, Shijun; Giannopoulos, Ioannis K.; Lin, Minxiao; Xiong, Yi; Liu, Yiding; Shen, Zhengquan
    This study presents findings for the strength and failure mechanism of a 3D-printed Continuous Carbon Fibre reinforced Onyx (CCF/Onyx) Thermo-Plastic Composite Fastener (TPCF) and a single lap-joint (SLJ) made of fibre/polymer composite reinforced by the TPCF. The study was carried out by numerical analysis and experiment methods including test sample design, manufacturing process and mechanical test. The 3D-printed fasteners were manufactured and tested in shear mode for two types of joining arrangement: fastened and hybrid bonded/fastened joints. Firstly, experiment was carried out for the TPCF fastened SLJ and the results show that addition of CCF in the Onyx matrix and post heat-treatment process could significant enhance the TPCF strength. The results was then benchmarked against a SLJ with steel fastening. The shear failure load of the SLJ reinforced by heat-treated CCF/Onyx TPCF of 8mm diameter was 36% lower than a SLJ reinforced by a steel bolt of the same size. Numerical model for progressive damage simulation was also created based on the failure theory from Puck and Schürmann achieving good correlation with the experimental data. Secondly, the TPCF fasteners were manufactured with two types of heat-treated countersunk head and pan head forming and used to reinforce bonded SLJ. The test results show that the bonded SLJ reinforced by the TPCF fastener of countersunk head is of 11.7% higher strength and an increase in ultimate deformation by 9.1% compared to a bonded SLJ reinforced by steel fastener of 5mm diameter. From the numerical and experimental study, it was noted that this was attributed to countersunk configuration to reduce out-out-plane bending and provide better crack arresting for the joint bonding.
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    Structure health monitoring of a composite wing based on flight load and strain data using deep learning method
    (Elsevier, 2022-01-29) Lin, Minxiao; Guo, Shijun; He, Shun; Li, Wenhao; Yang, Daqing
    An investigation was made into a method for Structural Health Monitoring (SHM) of a composite wing using Convolutional Neural Network (CNN) model. In this method, various aerodynamic loads of an aircraft during flight and corresponding strain data were used for CNN model training. The proposed method was demonstrated by numerical simulation using vortex lattice method for aerodynamic loads of an A350-type aircraft in over a thousand flight conditions and a Finite Element (FE) model as a digital twin of the full-scale composite wing. To represent the measurement of 324 sensors mounted in the 18 skin-rib joints of the inboard wing, strain data from the 18x18 elements of the FE model in the sensor locations were calculated corresponding to the flight loadings. The strain data from the original structure FE model were employed to train a CNN model that was classified as healthy samples. Damaged elements were then introduced in random locations to produce data samples corresponding to the same set of flight loads for the CNN model training. In the subsequent damage detection process using the trained CNN model, confusion matrix, uncertainty and sensitivity analysis were evaluated. The study results show that robust damage detection results can be obtained with 99% accuracy without noise and 97% accuracy with 2% Gaussian noise. In the damage localization process, threshold value was set at 1.5, 2 or 2.5, and 83% overall accuracy was achieved using the CNN model when the threshold value was 1.5. The study demonstrated that the proposed method is efficient, accurate and robust.