碳纖維增強(qiáng)樹脂基復(fù)合材料多向?qū)雍习邈娤髁︻A(yù)測(cè)
[Abstract]:Carbon fiber reinforced resin matrix composite (CFRP) has many excellent properties, such as specific strength, high specific stiffness, fatigue resistance and corrosion resistance, etc. In recent years, the amount of carbon fiber reinforced resin matrix composites has been increasing year by year in the field of aerospace and other fields. In the manufacturing process of composite components, milling has become an important processing method for composite components because of its flexible track, strong adaptability and high processing quality. Due to the typical anisotropy of carbon fiber reinforced composites, machining damage is very sensitive to the change of milling force, and excessive milling force can easily lead to delamination and collapse damage. Therefore, accurate prediction of milling force of composite material and control of milling force are the urgent problems to be solved in high quality and high efficiency milling of composite material. In this paper, carbon fiber reinforced resin matrix composite is used as the research material, the method of combining experimental research with theoretical analysis is used. Firstly, BP neural network is used and milling force coefficient is introduced to realize accurate prediction of CFRP unidirectional lamination milling force coefficient. On this basis, the relationship between milling force and unidirectional lamination of CFRP multidirectional laminates is analyzed, and the CFRP multidirectional milling force prediction model is established by using laminated stacking method. This paper provides a certain reference for predicting and controlling the milling force of CFRP in engineering practice. The main contents of this paper are as follows: firstly, the milling process experiment of CFRP unidirectional laminated plate is carried out, and the characteristics of milling force curve in time domain under different fiber directions are analyzed. According to the corresponding relationship between milling force time domain curve and cutting process, the data of milling force coefficient are obtained, and the change rule of milling force coefficient with fiber cutting angle, cutting thickness and cutting speed is obtained. Then, based on the data of milling force coefficient obtained, the prediction of milling force coefficient is realized by using BP neural network, and the experimental results show that the accuracy of the predicted CFRP milling force coefficient can reach more than 85%. Then, considering the effect of the interlaminar bonding force between the layers, the existing methods for calculating the milling force of multidirectional laminated plates with linear superposition unidirectional milling force are optimized. By observing the surface morphology and the underplane morphology of CFRP multidirectional laminates, it is inferred that due to the effect of interlaminar bonding force, 90 擄and 135 擄unidirectional laminates are strongly supported by adjacent layers, and the cutting state can be improved, and the corresponding milling force is smaller. 90 擄and 135 擄are proposed. The laying mode of unidirectional layer can be divided into two types: one side and the other side, and the corresponding optimization coefficient is fitted out by the experiment. Finally, using the data of milling force coefficient predicted by BP neural network and the superposition method considering the effect of interlaminar bonding force, the CFRP multidirectional milling force prediction model is established, and the precision of the model is verified by technological experiments. The results show that the CFRP multidirectional laminated plate prediction model is accurate and accurate.
【學(xué)位授予單位】:大連理工大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2016
【分類號(hào)】:TB332;TG54
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