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过渡区参数对TRB管液压胀形性能的影响及预测 被引量:9

Influence and prediction of transition zone parameters on hydraulic bulging properties for TRB tube
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摘要 建立了不同过渡区参数的TRB(Tailor-Rolled Blank)管液压胀形有限元模型,提出了一种离散不规则过渡区的新方法,研究了TRB管过渡曲线、过渡区长度和厚度差对胀形性能的影响规律。针对TRB管液压胀形,进行了正交设计,以数值模拟结果为基础,建立了BP神经网络预测模型,并将预测结果与数值模拟结果进行对比分析,验证了预测结果的精度。结果表明:各参数对胀形性能影响的区域不同,但都对最大成形高度影响显著,极差最大达6.47 mm,最小为2.88 mm;凹弧型过渡曲线的成形性能最差,最大的成形高度差为2.88 mm;增大过渡区长度有利于胀形,随厚度差增加,成形性能快速下降,其中厚侧成形高度差值达8.22 mm。单组预测值在误差范围内,预测模型能用于预测其他过渡区参数组合的TRB管胀形。 A FEM of tailor-rolled blank (TRB) tube with different transition zone parameters was established, and the new method of dis- crete irregular transition zone was proposed. Then, the influences of transition curve, thickness difference and length of transition zone for TRB tube on bulging properties were studied, and the orthogonal design was done for the TRB tube hydraulic bulging. Based on the result of numerical simulation, the BP neural network prediction model was established, and the predicted results were compared with the nu- merical simulation results to verify the accuracy of the predicted results. The result indicates that the parameters have different effects on the bulging proporties, but have a significant influence on the maximum forming height from 6.47 mm to 2. 88 mm. Furthermore, the con- cave arc transition curve has the worst forming properties with the max difference for forming height of 2. 88 mm, and the increase of tran- sition zone length is conductive to bulging. However, the forming performance decreases rapidly with the increase of thickness difference, and the forming height difference of the thick side is up to 8.22 ram. The single set of predicted value is within the margin of error, so the prediction model can be used to predict the bulging of TRB tube for other transition zone parameters.
出处 《锻压技术》 CAS CSCD 北大核心 2017年第11期99-104,共6页 Forging & Stamping Technology
基金 重庆市科委自然科学基金计划资助项目(cstc2012jj A70001)
关键词 TRB管 过渡区参数 液压胀形性能 过渡区离散方式 成形高度 BP神经网络 TRB tube transition zone parameters hydraulic bulging properties discrete way of transition zone forming height BP neural network
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