航空薄壁件铆接变形分析及预测研究

发布时间：2019-05-15 17:34

【摘要】：铆接是飞机装配中主要的连接形式之一。航空薄壁件单点铆接会产生局部微小变形,并在批量铆接过程中积累叠加,从而导致装配体整体产生扭曲和翘曲变形,影响飞机结构件的装配准确度以及疲劳寿命。新型超音速隐身飞机对结构的装配准确度、外形准确度和疲劳寿命要求进一步提高,必须严格控制铆接产生的变形。导致铆接变形的因素众多并相互交织,且各种因素对铆接变形的影响规律及其相互耦合关系极为复杂,局部单钉铆接引起的微小变形在批量铆接时的累加规律仍有待深入研究。因此,研究航空薄壁件铆接变形的预测理论、探索变形控制方法对于提高飞机装配技术水平具有重要的理论意义和工程应用价值。本文以“局部-整体”的研究思路,采用理论分析、数值计算、实验研究和智能优化等多种研究方法,从铆接变形行为的机理入手,对航空薄壁件的铆接变形机理和多铆钉结构变形累积规律进行深入研究,力图通过铆接工艺参数的控制达到对铆接变形的控制目的。论文的主要工作与创新如下:1、提出了基于接触关系的两阶段单钉铆接分析方法,建立了压铆力及干涉量与镦头几何尺寸关系解析模型、母材径向扩展量与压铆力及干涉量关系解析模型。以铆接过程中铆钉与母材的接触关系为判断依据,提出了单钉铆接过程的两阶段力学分析方法。以翁克索夫表面摩擦力分布理论为基础,充分考虑镦头变形的非均匀性,建立了镦头几何尺寸与压铆力关系解析模型。发现了铆接时镦头下方存在下压倾角,以体积不变原则建立了干涉量与镦头几何尺寸关系解析模型。忽略厚度方向应力对径向扩展的影响,以均匀干涉量为前提,利用厚壁筒受压理论,建立了薄壁件径向扩展变形与径向压应力关系解析模型。2、提出了基于思维进化算法优化BP神经网络的单钉铆接变形预测方法,建立了考虑回弹的基于半波压铆力加载的铆接变形数值计算模型。以静、动态力学性能试验及摩擦系数测定试验,建立了母材7075-T651和铆钉2A10-T4两种材料的Johnson-Cook本构模型,确定了两种材料间、铆钉与T8A铆模材料间的动摩擦系数。通过工程常用铆接参数设定,并考虑铆接回弹对变形的影响,建立了基于半波压铆力加载的铆接变形数值计算模型,获取了薄壁件变形的弹塑性边界范围以及应力、应变、位移的变化规律,结果表明薄壁件厚度方向变形量较径向扩展量大一个数量级,验证了镦头下压倾角的存在。选取铆接工艺人员可控制的压铆力、铆钉长度、钉/孔间隙三个工艺参数作为输入量,以干涉量、薄壁件随径向位置的厚度方向变形量为输出量,提出了基于思维进化算法优化BP神经网络的单钉铆接变形预测方法。3、研究了单钉单因素铆接工艺参数对薄壁件厚度方向变形的影响规律,根据单因素分析获得的最优取值,以铆钉间距、铆接顺序为变量,建立了双钉、三钉、四钉和十钉铆接结构的铆接变形数值计算模型。通过对铆接过程涉及的参数进行分类,确定影响铆接变形的关键工艺参数为压铆力、钉杆长度、钉/孔间隙等。结果表明:单钉单因素情况下,铆接工艺参数对薄壁件变形的影响存在最优取值;多钉铆接时,以薄壁件变形最小为目标,获得了铆钉间距与铆钉直径的最优比值。4、提出了粒子群/支持向量回归机的多钉铆接变形预测及优化方法。以铆接顺序为输入量,薄壁件厚度方向最大变形为输出量,提出了粒子群优化支持向量回归机的多钉铆接薄壁件变形预测方法。根据铆接工艺参数对薄壁件厚度方向变形的影响规律,建立了十钉双排结构的铆接变形预测模型。在预测模型经实验验证了正确性的基础上,以薄壁件最大变形最小化为目标,提出了基于粒子群算法的铆接顺序优化方法以控制铆接引起的变形,并通过实验验证了该智能算法对于解决多钉铆接顺序优化问题的有效性。
[Abstract]:Riveting is one of the main forms of connection in aircraft assembly. The single-point riveting of the aviation thin-wall piece can produce the local micro-deformation and accumulate the superposition during the batch riveting process, thus leading to the distortion and the warping deformation of the whole assembly body, thus affecting the assembly accuracy and the fatigue life of the aircraft structural member. The assembly accuracy, appearance accuracy and fatigue life requirement of the new supersonic stealth aircraft are further improved, and the deformation of the riveting must be strictly controlled. The factors that lead to the riveting deformation are many and are intertwined, and the influence of various factors on the riveting deformation and the mutual coupling relation are very complicated. The accumulation law of the micro-deformation caused by the local single-nail riveting still needs to be researched deeply. Therefore, the research of the prediction theory of the riveting deformation of the aviation thin-wall parts, and the exploration of the deformation control method have important theoretical and engineering application value for improving the aircraft assembly technology level. In this paper, by means of the research of the "local-integral", a variety of research methods such as the theoretical analysis, the numerical calculation, the experimental research and the intelligent optimization are adopted to study the riveting deformation mechanism and the deformation accumulation rule of the multi-rivet structure from the mechanism of the riveting deformation behavior. The control of the riveting deformation is to be achieved by the control of the riveting process parameters. The main work and innovation of the paper are as follows:1. The two-stage single-nail riveting analysis method based on the contact relation is put forward, and the analytical model of the pressure-riveting force and the interference quantity and the geometric dimension of the hammer head is established, and the relationship between the radial expansion of the base material and the pressure-riveting force and the interference quantity is established. Based on the contact relation between the rivet and the base material during the riveting process, the two-stage mechanical analysis method of the single-screw riveting process is put forward. On the basis of the theory of the friction force distribution on the surface of the Ong Sov, the non-uniformity of the deformation of the head is fully considered, and the analytical model of the relation between the geometric dimension of the head and the pressure-riveting force is established. An analytical model of the relationship between the amount of interference and the geometric dimension of the head is established by the principle of volume change. The influence of the stress on the radial expansion in the thickness direction is neglected, and the radial expansion deformation of the thin-wall part and the relation analysis model of the radial compressive stress are established by using the pressure theory of the thick-wall cylinder based on the pressure theory of the thick-wall cylinder. The method of single-screw riveting deformation prediction based on the optimization of BP neural network based on the thought evolution algorithm is put forward, and the numerical calculation model of the riveting deformation based on half-wave pressure riveting is established. The Johnson-Cook constitutive model of the two materials of the base metal 7075-T651 and the rivet 2A10-T4 was established by the static, dynamic mechanical property test and the friction coefficient determination test. The dynamic friction coefficient between the two materials, the rivet and the T8A rivet material was determined. By setting the common riveting parameters of the project and considering the influence of the riveting rebound on the deformation, a numerical calculation model of the riveting deformation based on the half-wave pressure riveting force loading is established, the elastic-plastic boundary range of the deformation of the thin-wall part and the change law of the stress, the strain and the displacement are obtained, The results show that the deformation in the thickness direction of the thin-wall piece is an order of magnitude larger than that of the radial expansion, and the existence of the dip angle of the pressure head is verified. the pressure riveting force, the rivet length, the nail/ hole gap and the three process parameters which can be controlled by the riveting process personnel are selected as the input quantity, the amount of deformation of the thin-wall part along with the thickness direction of the radial position is the output quantity, A method of single-screw riveting deformation prediction based on the optimization of BP neural network based on the thought evolution algorithm is presented.3. The influence of single-nail single factor riveting process parameters on the deformation of the thickness direction of the thin-wall part is studied. The optimum value obtained from the single-factor analysis is obtained, and the rivet pitch and the riveting sequence are the variables. The numerical calculation model of the riveting deformation of the two-nail, three-nail, four-nail and ten-nail riveting structure was established. By classifying the parameters involved in the riveting process, the key process parameters affecting the riveting deformation are the pressure riveting force, the length of the nail rod, the nail/ hole gap, and the like. The results show that the influence of the riveting process parameters on the deformation of the thin-walled part is the best value in the case of single-nail, and the optimal ratio of the rivet spacing to the diameter of the rivet is obtained when the multi-nail is riveted, and the optimal ratio of the rivet spacing and the diameter of the rivet is obtained. In this paper, a multi-pin riveting deformation prediction and optimization method for particle swarm/ support vector regression machine is proposed. In this paper, the deformation prediction method of multi-pin riveting thin-wall parts of the particle swarm optimization support vector regression machine is put forward, in order of the input quantity and the maximum deformation of the thickness direction of the thin-wall piece as the output quantity. According to the influence law of the riveting process parameters on the thickness direction deformation of the thin-wall part, a riveting deformation prediction model of the ten-nail double-row structure is established. Based on the experimental verification of the prediction model, the optimization method of the riveting sequence based on the particle swarm optimization is proposed to control the deformation caused by the riveting. And the effectiveness of the intelligent algorithm for solving the problem of multi-pin riveting sequence optimization is verified by experiments.
【学位授予单位】：西北工业大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：V262.4

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