According to the development trend of modern car body, the future car body will be composed of steel, aluminum‐magnesium alloy, plastic, carbon fiber reinforced polymer (CFRP), and other lightweight materials. Hybrid material body represents the latest development trend of car body structure in the future. With the development of lightweight technology, the application of carbon fiber composite material in automobile bodies is increasing due to its exceptional performance. Therefore, the joining of carbon fiber sheets with dissimilar sheets process in the hybrid material body is a key technical problem that needs to be solved. In this paper, the existing joining processes of adhesive process, mechanical joining, welding process, and hybrid joining for carbon fiber composites are reviewed; the principle, development, advantages, and shortcomings of each process for carbon fiber composites joining processes are systematically described; and the latest progress of the joining process are introduced. The purpose is tantamount to broaden the application range of carbon fiber sheets with dissimilar sheets and provide reference for its extensive application in the development of hybrid car body and lightweight.

Aiming at the problem of the fatigue life prediction of rubber under the influence of temperature, the effects of thermal ageing and fatigue damage on the fatigue life of rubber under the influence of temperature are analysed and a fatigue life prediction model is established by selecting strain energy as a fatigue damage parameter based on the uniaxial tensile data of dumbbell rubber specimens at different temperatures. Firstly, the strain energy of rubber specimens at different temperatures is obtained by the Yeoh model, and the relationship between it and rubber fatigue life at different temperatures is fitted by the least-square method. Secondly, the function formula of temperature and model parameters is obtained by the least-square polynomial fitting. Finally, another group of rubber specimens is tested at different temperatures and the fatigue characteristics are predicted by using the proposed prediction model under the influence of temperature, and the results are compared with the measured results. The results show that the predicted value of the model is consistent with the measured value and the average relative error is less than 22.26%, which indicates that the model can predict the fatigue life of this kind of rubber specimen at different temperatures. What's more, the model proposed in this study has a high practical value in engineering practice of rubber fatigue life prediction at different temperatures.

Fused deposition modeling (FDM) has been one of the most widely used rapid prototyping (RP) technologies leading to the increase in market attention. Obviously it is desirable to print 3D objects; however, existing FDM printers are restricted to printing only monochrome objects because of the entry-level nozzle structure, and literature on the topic is also sparse. In this paper, the CAD model of the nozzle is established first by UG (Unigraphics NX) software to show the structure of fused deposition modeling 3D printer nozzle for color mixing. Second, the flow channel model of the nozzle is extracted and simplified. Then, the CAD and finite element model are established by UG and ICEM CFD software, respectively, to prepare for the simulation. The flow field is simulated by Fluent software. The nozzle's suitable temperature at different extrusion speeds is obtained, and the reason for the blockage at the intersection of the heating block is revealed. Finally, test verification of the nozzle is performed, which can produce mixed-color artifacts stably.

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PurposeIntelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approachIn this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.FindingsThe Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.Originality/valueThe fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.