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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Computing the tensile behaviour of tailor welded blanks made of dual-phase steel by neural network-based expert system
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Computing the tensile behaviour of tailor welded blanks made of dual-phase steel by neural network-based expert system

机译：基于神经网络的专家系统计算双相钢拼焊板的拉伸性能

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摘要

This work primarily aims to develop an expert system based on the artificial neural network (ANN) to predict the tensile behaviour of tailor welded blanks (TWBs) made of dual-phase (DP) 590 steel. The work also aims to compare the predictions by ANN models with empirical models and the size of the training data set of the prediction accuracy of these models. The strain hardening exponent 'n' and strength coefficient 'K' are predicted. The results obtained from expert system and empirical models are validated by comparing them with the results obtained from finite element simulations and experiments. It is observed that expert system/ANN predictions based on the full factorial design of experiments (DOE) is better than the ANN predictions based on the orthogonal array and predictions based on the empirical models. With the reduced orthogonal training data, ANN model-based predictions are more accurate than the empirical models in most of the test cases taken outside the training range. Inverse models for predicting the TWB parameter combination for good tensile characteristics are also developed and show promising results. The ANN/expert system developed through full factorial DOE is comparable with the experimental results. The ANN/expert system developed through orthogonal DOE is not comparable with the experimental results.
机译：这项工作的主要目的是开发基于人工神经网络（ANN）的专家系统，以预测由双相（DP）590钢制成的拼焊板（TWB）的拉伸性能。这项工作还旨在将ANN模型的预测与经验模型以及这些模型的预测准确性的训练数据集的大小进行比较。预测了应变硬化指数“ n”和强度系数“ K”。通过将专家系统和经验模型获得的结果与有限元模拟和实验获得的结果进行比较，可以对它们进行验证。可以观察到，基于实验的全因子设计（DOE）的专家系统/ ANN预测要优于基于正交数组的ANN预测和基于经验模型的预测。在正交训练数据减少的情况下，在训练范围之外进行的大多数测试案例中，基于ANN模型的预测比经验模型更准确。还开发了用于预测TWB参数组合以获得良好拉伸特性的逆模型，并显示出令人鼓舞的结果。通过全因子DOE开发的ANN /专家系统与实验结果相当。通过正交DOE开发的ANN /专家系统无法与实验结果进行比较。

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