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Artificial neural network (ANN) approach to predicting micro hardness profile values of iron-based sintered alloys

机译:人工神经网络(ANN)预测铁基烧结合金微硬度分布值的方法

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摘要

Recent interest in artificial neural networks has considerably extended their use in the field of powder metallurgy. Advanced in the paper is a model for predicting the micro hardness of sintered compacts made from iron powders and powder mixtures through the process of sintering performed in different atmospheres. The proposed model is based on three layer neural network with backpropagation learning algorithm. Specially developed software has been used to provide for the proper functioning of the neural network. Moreover, it should also be noted that the training data used to carry out the research has been collected by a laboratory controlled experimental testing. Finally, the paper concludes that the presented neural network model is applicable for hardness profile prediction of iron-based sintered alloys as confirmed by the experimental results.
机译:最近对人工神经网络的兴趣大大扩展了它们在粉末冶金领域的使用。本文的先进是一种用于预测通过在不同大气中进行的烧结过程中由铁粉和粉末混合物制成的烧结块的微硬度的模型。该建议的模型基于具有BackProjagation学习算法的三层神经网络。专门开发的软件已被用于提供神经网络的正常运行。此外,还应注意,通过实验室控制的实验测试收集了用于开展研究的培训数据。最后,本文得出结论,所提出的神经网络模型适用于通过实验结果证实的铁基烧结合金的硬度谱预测。

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