ABSTRACT
Modern highly integrated microelectronic products often face the challenge of internal heat dissipation, leading to a significant decrease in their operational efficiency. Annealed Pyrolytic Graphite (APG), due to its superior thermal conductivity, has garnered attention from researchers. The interface thermal resistance between APG and supporting materials like copper significantly affects heat transfer during APG's operation. Existing studies rarely delve into the influence of factors such as the shape of APG material interfaces on thermal resistance from a microscopic perspective. In this paper, utilizing transient thermo-reflectance method and non-equilibrium molecular dynamics simulations, the interface thermal resistance of the APG-Cu structure was investigated under different conditions. The impact of parameters such as copper thickness, interface micro-surface morphology, and APG thickness on the calculated interface thermal resistance was examined. Simulation results revealed that copper thickness had a minor effect on the interface thermal resistance. This is because the phonon participation ratio remains unaffected by changes in the thickness of the copper layer. The interfacial thermal resistance beneath microscopically cylindrical copper surfaces was considerably lower than that of rectangular copper surfaces. This is because beneath the cylindrical surface, the enlarged interface contact area facilitates enhanced thermal transport between the interfaces. The computed results of the radial distribution function in the paper also indirectly validate this viewpoint. The magnitude of interfacial thermal resistance for different APG layers was influenced by the coupling effect of intermolecular forces and the layered stacking structure of APG. The interfacial thermal resistance under the condition of three layers of APG reaches its minimum value, which is 2.2 × 10-9 (K m2 W-1). Furthermore, from the phonon perspective, it is found that the interfacial thermal resistance with different numbers of APG layers is closely related to the localization or delocalization state of phonons. As the number of APG layers increased, the interface thermal resistance showed a trend of initial decrease followed by an increase, this is because the average phonon participation ratio increases and then decreases with the number of APG layers. The average phonon participation ratio reaches its maximum value of 0.45778 under the condition of three layers of APG.
ABSTRACT
At present, the detection accuracy of bolt-loosening diagnoses is still not high. In order to improve the detection accuracy, this paper proposes a fault diagnosis model based on the TSCNN model, which can simultaneously extract fault features from vibration signals and time-frequency images and can precisely detect the bolt-loosening states. In this paper, the LeNet-5 network is improved by adjusting the size and number of the convolution kernels, introducing the dropout operation, and building a two-dimensional convolutional neural network (2DCNN) model. Combining the advantages of a one-dimensional convolutional neural network (1DCNN) with wide first-layer kernels to suppress high-frequency noise, a two-stream convolutional neural network (TSCNN) is proposed based on 1D and 2D input data. The proposed model uses raw vibration signals and time-frequency images as input and automatically extracts sensitive features and representative information. Finally, the effectiveness and superiority of the proposed approach are verified by practical experiments that are carried out on a machine tool guideway. The experimental results show that the proposed approach can effectively achieve end-to-end bolt-loosening fault diagnoses, with an average recognition accuracy of 99.58%. In addition, the method can easily achieve over 93% accuracy when the SNR is over 0 dB without any denoising preprocessing. The results show that the proposed approach not only achieves high classification accuracy but also has good noise immunity.
