碳纤维增强树脂基复合材料多向层合板铣削力预测

发布时间：2018-08-14 16:31

【摘要】：碳纤维增强树脂基复合材料(CFRP)具有比强度、比刚度高,以及耐疲劳、耐腐蚀等优良特性,近年来在航空航天等领域的用量逐年增多。在复合材料构件制造过程中,铣削加工由于加工轨迹灵活、适应性强、加工质量高,已成为复合材料构件的重要加工手段。由于碳纤维增强复合材料呈典型的各向异性,加工损伤对铣削力变化极为敏感,过大的铣削力极易导致分层、崩边等损伤,因此实现对复合材料铣削力的准确预测进而控制铣削力成为复合材料高质高效铣削加工亟待解决的问题。本文以碳纤维增强树脂基复合材料为研究材料,使用实验研究与理论分析相结合的方法,首先使用BP神经网络并引入铣削力系数实现了对CFRP单向铺层铣削力系数的准确预测；在此基础上分析了CFRP多向层合板的铣削力与单向铺层之间的关系,使用层合叠加的方法建立了CFRP多向层合板铣削力预测模型,为工程实际中预测并控制CFRP铣削力提供了一定参考,本文主要研究内容如下：首先开展CFRP单向层合板铣削工艺实验,分析了不同纤维方向角下铣削力时域曲线特征；根据铣削力时域曲线与切削过程的对应关系,计算得到铣削力系数数据,获得了铣削力系数随纤维切削角、切削厚度、切削速度的变化规律。而后,基于已获得的铣削力系数数据,使用BP神经网络,实现了对铣削力系数的预测,并进行了实验验证,结果表明,预测的CFRP铣削力系数准确度可达85%以上。然后,考虑各铺层间的层间结合力的作用,对现有的线性叠加单向铺层铣削力计算多向层合板铣削力的方法进行了优化。通过观测CFRP多向层合板加工表面形貌和面下形貌,推断出由于层间结合力作用,使得90°和135°单向铺层受到了相邻铺层较强的支撑作用,切削状态得以改善,对应的铣削力较小,提出将90°和135。单向铺层的铺放方式分为单侧异向支撑和双侧异向支撑两种,并进行工艺实验,拟合出对应的优化系数。最后,使用BP神经网络预测出的铣削力系数数据和考虑铺层间结合力作用的叠加方法建立CFRP多向层合板铣削力预测模型,并进行工艺实验对模型精度进行验证,结果表明建立的CFRP多向层合板预测模型精度较高,预测结果较为准确。
[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 掳and 135 掳unidirectional laminates are strongly supported by adjacent layers, and the cutting state can be improved, and the corresponding milling force is smaller. 90 掳and 135 掳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.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TB332;TG54

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