基于混合硬化模型的TRIP780高强钢双C梁扭曲回弹仿真与试验

发布时间：2018-06-29 05:10

  本文选题：混合硬化模型 + TRIP　； 参考：《工程设计学报》2017年06期

【摘要】：为了实现扭曲回弹的精确预测,在高强度钢板冲压成形中必须对工艺参数进行有效控制。基于试验数据,利用响应面法和遗传算法对Voce非线性各向同性硬化模型和Chaboche非线性随动硬化模型组成的混合硬化模型参数进行反求。基于三维空间两异面直线夹角,提出一种评价扭曲回弹的指标。利用液压机进行TRIP780高强钢双C梁扭曲回弹试验,并且利用三坐标测量仪测量扭曲回弹角。利用基于双C梁有限元模型建立的回弹角响应面模型预测值逼近回弹角的测量值,从而确定混合硬化模型参数。为了实现扭曲回弹的精确预测,在冲压成形中必须对有效的工艺参数进行控制。基于已验证的双C梁有限元模型,利用极差分析方法对相关因素进行分析,确定影响扭曲回弹的关键因素。研究结果表明,对扭曲回弹影响较大的因素依次为摩擦系数μ、压边力FN、凹模圆角半径R1和R2,为扭曲回弹的有效控制提供一定的理论依据。
[Abstract]:In order to predict the distortion springback accurately, the process parameters must be controlled effectively in high strength steel sheet stamping. Based on the experimental data, the parameters of the mixed hardening model composed of Voce nonlinear isotropic hardening model and Chaboche nonlinear servo hardening model are obtained by using response surface method and genetic algorithm. Based on the angle between the two cross-plane lines in three dimensional space, an index to evaluate the distortion springback is proposed. The twisting springback test of TRIP780 high strength steel double C beam was carried out by hydraulic press, and the torsional springback angle was measured by three coordinate measuring instrument. The predictive value of the springback angle response surface model based on the finite element model of double C beam is used to approximate the measured value of the springback angle, and the parameters of the mixed hardening model are determined. In order to predict the distortion springback accurately, the effective process parameters must be controlled in stamping forming. Based on the verified double C beam finite element model, the relative factors are analyzed by using the range analysis method, and the key factors affecting the distortion springback are determined. The results show that the factors which have a great effect on the twist springback are friction coefficient 渭, blank holder force FN, die radius R 1 and R 2, which provides a theoretical basis for the effective control of twist springback.
【作者单位】： 西南交通大学机械工程学院;
【基金】：国家自然科学基金资助项目(51005193) 国家大学生创新创业训练计划项目(201710613033)
【分类号】：TG386
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本文编号：2080989