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Methodology of the mechanical properties prediction for the metallurgical products from the engineering steels using the Artificial Intelligence methods

机译:用人工智能方法预测工程钢的冶金产品力学性能的方法

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The paper presents the new method for forecasting the yield point and the ultimate tensile strength for steel. These parameters are calculated basing on the chemical composition and technological factors of steel manufacturing. The artificial neural network technology was used for development of models making prediction of these properties possible. Software was developed, basing on these models, searching for the optimum chemical composition of steel, so that - at the particular conditions of the technological process - the risk of manufacturing the products that would not meet the requirements of the pertinent standards would be minimised. Search for the optimum chemical composition makes use of the genetic algorithms.
机译:本文提出了一种预测钢的屈服点和极限抗拉强度的新方法。这些参数是根据钢铁制造的化学成分和技术因素计算得出的。人工神经网络技术用于模型开发,从而可以预测这些属性。在这些模型的基础上开发了软件,以寻找钢的最佳化学组成,以便在工艺过程的特定条件下,将制造不符合相关标准要求的产品的风险降到最低。寻找最佳的化学组成利用遗传算法。

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