CFRP/6061-T6胶铆接头强度的工艺参数影响分析与预测

Analysis and prediction of influence of process parameters on strength of CFRP/6061-T6 adhesive riveting joint

采用正交试验设计方法,研究工艺参数对碳纤维增强复合材料(Carbon Fiber Reinforced Polymer, CFRP)和铝合金(6061-T6)胶铆接头强度的影响,以胶铆接头失效载荷为目标量,以铆接压强、CFRP板厚、搭接宽度及铝板表面处理作为影响目标量的4个因素,采用灰色关联度结合主成分分析法对CFRP/6061-T6胶铆接头的4个工艺参数进行了多目标分析。在此基础上,采用遗传算法优化的BP神经网络(GA-BP神经网络)方法,建立了工艺参数与接头强度的预测模型。研究结果表明:4个工艺参数中,铝合金的表面处理对接头强度的影响程度最大,CFRP板厚次之,然后是搭接宽度,铆接压强对接头强度的影响最小。GA-BP神经网络模型的预测结果与试验结果接近,拟合程度高,表明GA-BP神经网络模型能够对CFRP/6061-T6胶铆接头强度进行有效预测。

An orthogonal experimental design was used to study the influence of process parameters on the strength of carbon fiber reinforced polymer(CFRP) and aluminum alloy 6061-T6 riveting-bonded joints. The failure load of the rubber riveting head was taken as the target quantity, and the riveting pressure, CFRP plate thickness, lap width and aluminum plate surface treatment were taken as the four factors affecting the target quantity. A multi-objective analysis of the four process parameters of the CFRP/6061-T6 riveting-bonded joints was conducted using gray correlation combined with principal component analysis. On this basis, the BP neural network(GA-BP) optimized by genetic algorithm was used to establish the prediction model of process parameters and joint strength. The results show that the surface treatment of aluminum alloy has the greatest effect on the joint strength, followed by the thickness of CFRP plate, then the lap width, and the riveting pressure is the least. The prediction results of GA-BP neural network model are close to the experimental results and the fitting degree is high. Therefore, GA-BP neural network model can effectively predict the strength of CFRP/6061-T6 riveting-bonded joint.