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首页> 外文期刊>ISIJ international >Neural Network Prediction of Hardness in HAZ of Temper Bead Welding Using the Proposed Thermal Cycle Tempering Parameter (TCTP)
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Neural Network Prediction of Hardness in HAZ of Temper Bead Welding Using the Proposed Thermal Cycle Tempering Parameter (TCTP)

机译：使用建议的热循环回火参数（TCTP）的神经网络预测回火焊缝热影响区的硬度

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摘要

A new thermal cycle tempering parameter (TCTP) to characterize the tempering effect during multi-pass thermal cycles has been proposed by extending the Larson-Miller parameter (LMP) to non-isothermal heat treatment. Experimental results revealed that the hardness in synthetic HAZ of low-alloy steel subjected to multi-pass tempering thermal cycles has a good linear relationship with the TCTP. The new hardness prediction system was constructed by using a neural network taking into consideration of the tempering effect during multi-pass welding, estimated by using the TCTP. Based on the thermal cycles numerically obtained by FEM and the experimentally obtained hardness database, the hardness distribution in HAZ of low-alloy steel welded with temper bead welding method was calculated. The predicted hardness was in good accordance with the experimental results. It follows that our new prediction system is effective for estimating the tempering effect in HAZ during multi-pass welding and hence enables us to assess the effectiveness of temper bead welding.
机译：通过将Larson-Miller参数（LMP）扩展到非等温热处理，已经提出了一种新的热循环回火参数（TCTP）来表征多道次热循环中的回火效果。实验结果表明，经过多次回火热循环的低合金钢合成热影响区的硬度与TCTP具有良好的线性关系。通过使用神经网络构建新的硬度预测系统，其中考虑了通过TCTP估算的多道次焊接过程中的回火效果。基于有限元数值模拟得到的热循环和实验获得的硬度数据库，计算了回火堆焊法焊接低合金钢在热影响区的硬度分布。预测的硬度与实验结果非常吻合。因此，我们的新预测系统可以有效地估算多道次焊接中热影响区的回火效果，因此使我们能够评估回火焊道焊接的有效性。

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