...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational Materials Science >A comparative study on Arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behaviour in 12Cr3WV steel
【24h】

A comparative study on Arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behaviour in 12Cr3WV steel

机译：Arrhenius型本构方程与人工神经网络模型预测12Cr3WV钢高温变形行为的比较研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The hot compressive deformation behaviour in 12Cr3WV steel was conducted on a Gleeble-1500 thermo-mechanical simulator at the temperature range of 1223-1373 K with the strain rate in the range of 0.01-30 s ~(-1) and the height reduction of 60%. Based on the experimental results, strain compensated Arrhenius-type constitutive equations and an artificial neural network (ANN) model with a back-propagation learning algorithm were developed for the characterization and prediction of the high-temperature deformation behaviour in the steel. And then a comparative predictability of the constitutive equations and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE) and the relative error. For the constitutive equations, R and AARE were found to be 0.9952% and 3.48% respectively, while for the ANN model, 0.9998 and 0.58% respectively. The relative errors between experimental and predicted flow stress computed from the constitutive equations and ANN model were respectively in the range of -15.46% to 10.46% and -4.12% to 4.08%. Moreover, the relative error within ±1% was observed for more than 85% of the test data sets of ANN model, while only 32% of the test data sets for the constitutive equations. The results indicate that the trained ANN model is more efficient and accurate in predicting the hot compressive behaviour in 12Cr3WV steel than the Arrhenius-type constitutive equations.
机译：在Gleeble-1500热力机械模拟器上在1223-1373 K的温度范围内进行12Cr3WV钢的热压缩变形行为，应变速率在0.01-30 s〜（-1）范围内，并且高度降低。 60％。根据实验结果，开发了应变补偿的Arrhenius型本构方程和带有反向传播学习算法的人工神经网络（ANN）模型，用于表征和预测钢中的高温变形行为。然后，根据相关系数（R），平均绝对相对误差（AARE）和相对误差，进一步评估了本构方程和训练后的ANN模型的可比较性。对于本构方程，R和AARE分别为0.9952％和3.48％，而对于ANN模型，分别为0.9998和0.58％。由本构方程和人工神经网络模型计算得到的实验应力与预测流体应力之间的相对误差分别在-15.46％至10.46％和-4.12％至4.08％的范围内。此外，对于超过85％的ANN模型测试数据集，观察到的相对误差在±1％以内，而对于本构方程，只有32％的测试数据集。结果表明，与Arrhenius型本构方程相比，训练后的ANN模型在预测12Cr3WV钢的热压缩行为方面更为有效和准确。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号