U形件弯曲回弹的数值模拟及预测与优化控制

【作者】 赵叶锋；

【导师】 陈靖芯；

【作者基本信息】 扬州大学 ， 机械制造及其自动化， 2005， 硕士

【摘要】 弯曲是成形工序中最为普遍的成形方法之一,应用相当广泛,在冲压成形中占有很大比例,在汽车冲压件中就有许多零件主要是通过弯曲成形得到的,如纵梁、横梁、内外板等。板料弯曲成形后由于弹性回复和残余应力的作用不可避免地会产生回弹现象,回弹量的大小直接影响着零件的成形质量,以及后续的装配。经过十多年的研究和发展,目前回弹的预测、测量、控制及补偿的研究都还只是处于初级水平。因此,研究如何在设计模具前准确掌握回弹的规律及大小、如何确定模具的结构、如何选取成形过程中的冲压参数等问题对实际生产过程中减小和控制板料冲压成形中的回弹具有重要的指导意义。本文将数值模拟技术、正交诉验方法、人工神经网络和最优化技术综合应用于U形件弯曲回弹的预测、优化和控制中,为实际生产中解决回弹问题、提高零件成形质量、缩短新产品开发周期提出了新思路。 本文的研究工作主要有以下几个方面: 对板料冲压成形过程和回弹数值模拟中的关键技术进行了研究,如:有限元算法、单元类型和网格划分技术、单元公式的选择、接触处理和磨擦模型、材料模型和回弹模拟方法。此外,还对回弹数值模拟的建模步骤和方法进行了介绍。 对弯曲变形过程和回弹进行了细致地讨论和研究。通过U形件弯曲回弹的数值模拟试验,研究了冲压成形参数(如:摩擦系数、压边力、板料厚度、模具间隙等)对回弹的影响,并且通过正交试验方法研究了板料厚度、模具间隙、压边力及其两两交互作用对U形件弯曲回弹影响规律和显著性,根据研究结果对影响U形件弯曲回弹的主要因素进行了优化。 在U形件弯曲回弹数值模拟试验的基础上,利用BP神经网络建立了U形件弯曲回弹和板料厚度的预测模型,并根据神经网络算法拟合了模具间隙、压边力与回弹,板料厚度之间的非线性关系。 综合运用数值模拟试验、人工神经网络技术和最优化设计,对U形件弯曲回更多还原

【Abstract】 Bending, which possesses a great proportion in sheet metal forming, is one of the most prevalent forming methods. Among stamping parts of automobiles a lot of parts are mainly produced by bending, for instance, carlines, cross rails and inner/outer plates etc. Springback is the inevitable phenomenon caused by elastic recovery and residual stress after sheet metal bending forming, and it influences the forming quality and latter assembly of part. Through more than ten years’ researches and development, the current researches on springback prediction, measure, control and compensation are on a primary level. So it is significant to investigate how to find springback laws and values out accurately, and how to determine the structure of dies and select the stamping forming parameters and so on before designing moulds, which can reduce and control springback in sheet metal stamping processes. The combination of numerical simulation, orthogonal experimental method, ANN (Artificial Neural Network) and optimum technology is applied to investigate the springback prediction, optimization and control of U-shaped part bending in this paper, which can provide a new method to solve the springback problem, to improve the shape quality of the parts and to shorten the development period in practical production.The main contents of this dissertation are as follows:Key techniques of sheet metal forming and springback simulation are expounded such as finite element method, element type and mesh generation method, selection of element formula, contact algorithm and friction model, material model, springback simulation method. Furthermore, the modeling approach of springback numerical simulation is presented.The deformation process and springback of bending are discussed. A series of springback numerical simulation experiments of U-shaped part are carried out. The factors influencing sheet springback are analyzed in this paper, which include friction coefficient, binder force, sheet thickness and die gap etc. And then, the influence and notability of blank thickness, die gap, binder force and their two-factor interaction on bending springback of U-shaped part are studied by the orthogonal experimental design method. According to the analysis results of the orthogonal experimental design method, the key factors affecting bending springback of U-shaped part are optimized.Based on the numerical simulation experiments of U-shaped part bending, the prediction model of bending springback and blank thickness of U-shaped part is established by using BP neural network technology. The nonlinear relation between die gap, binder force and springback, blank thickness is fitted.Applied synthetically numerical simulation experiments, ANN and optimum technology, the springback optimization and control method of U-shaped part isexplored elementarily. Based on the study of bending springback of U-shaped part, springback optimization of U-shaped crashbox of vehicle body panels in product experiments is researched.The study on bending springback of U-shaped part can not only solve the springback problem like U-shaped parts, but also provide analyzing and solving methods to springback of complicated parts such as automotive panels.更多还原