Abstract An artificial neural network (ANN) model was developed to predict tensile and impact properties of a submerged arc helical welded (SAHW) pipeline steel API X70 based upon its chemical composition. Weight percent of the elements was considered as the input, while the tensile and Charpy impact properties were considered as the outputs. Scatter diagrams and two statistical parameters (absolute fraction of variance and relative error) were used to evaluate the prediction performance of the developed artificial neural network model. The predicted values were found to be in excellent agreement with the experimental data and the current model has a good learning precision and generalization (for training, validation and testing data sets). The results revealed that the developed model is very accurate and has a strong potential for capturing the interaction between the mechanical properties and chemical composition of welded high strength low alloy (HSLA) steels.

中文翻译：

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使用神经网络建模预测焊接钢 X70 管道的力学性能

摘要 开发了人工神经网络 (ANN) 模型来预测埋弧螺旋焊接 (SAHW) 管线钢 API X70 基于其化学成分的拉伸和冲击性能。元素的重量百分比被认为是输入，而拉伸和夏比冲击性能被认为是输出。散点图和两个统计参数（方差的绝对分数和相对误差）用于评估开发的人工神经网络模型的预测性能。发现预测值与实验数据非常吻合，当前模型具有良好的学习精度和泛化性（用于训练、验证和测试数据集）。