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首页> 外文期刊>Arabian Journal for Science and Engineering >Artificial Neural Network-Based Modeling for Impact Energy of Cast Duplex Stainless Steel
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Artificial Neural Network-Based Modeling for Impact Energy of Cast Duplex Stainless Steel

机译：基于人工神经网络的双相不锈钢冲击能建模

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摘要

The exploitation of artificial neural network as a computational technique in predicting the impact energy of cast duplex stainless steels based on its chemical composition is reported in this research work. Two hundred and twenty melts of duplex stainless steel of different compositions were casted, heat-treated and tested for Charpy impact test. A multilayer feed forward ANN model was developed based on 75% of the available chemical compositions of duplex stainless steel as input and impact energy in joules as output. The prediction efficiency of the developed models was calculated based on mean absolute error and mean absolute percentage error; the best model thus sorted out was validated and tested. A multilayer feed forward ANN model with two hidden layers was selected which provided better linear correlation between the chemical composition and impact energy. Correlation performance of considered ANN model with network topology expressed in terms of mean absolute percent error was found to be 0.43% with a correlation coefficient value of 0.95714. Testing and evaluation of the developed model proved to be efficient enough for the development of duplex stainless steels with required impact toughness.
机译：这项研究工作报道了利用人工神经网络作为一种计算技术，根据其化学成分预测铸造双相不锈钢的冲击能。铸造，热处理了220个不同成分的双相不锈钢熔体，并进行了夏比冲击试验。基于双相不锈钢可用化学成分的75％作为输入，而冲击能量以焦耳为输出，开发了多层前馈ANN模型。根据平均绝对误差和平均绝对百分比误差计算开发模型的预测效率；如此挑选出的最佳模型已经过验证和测试。选择了具有两个隐藏层的多层前馈ANN模型，该模型在化学成分和冲击能之间提供了更好的线性相关性。发现以平均绝对误差百分比表示的经过考虑的ANN模型与网络拓扑的相关性能为0.43％，相关系数值为0.95714。已开发模型的测试和评估证明对于开发具有所需冲击韧性的双相不锈钢足够有效。

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