Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

Maximilian Brait, Eduard Koppensteiner, Gerhard Schindelbacher, Jiehua Li, P. Schumacher
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Abstract

The complex metallurgical interrelationships in the production of ductile cast iron can lead to enormous differences in graphite formation and local microstructure by small variations during production. Artificial intelligence algorithms were used to describe graphite formation, which is influenced by a variety of metallurgical parameters. Moreover, complex physical relationships in the formation of graphite morphology are also controlled by boundary conditions of processing, the effect of which can hardly be assessed in everyday foundry operations. The influence of relevant input parameters can be predetermined using artificial intelligence based on conditions and patterns that occur simultaneously. By predicting the local graphite formation, measures to stabilise production were defined and thereby the accuracy of structure simulations improved. In course of this work, the most important dominating variables, from initial charging to final casting, were compiled and analysed with the help of statistical regression methods to predict the nodularity of graphite spheres. We compared the accuracy of the prediction by using Linear Regression, Gaussian Process Regression, Regression Trees, Boosted Trees, Support Vector Machines, Shallow Neural Networks and Deep Neural Networks. As input parameters we used 45 characteristics of the production process consisting of the basic information including the composition of the charge, the overheating time, the type of melting vessel, the type of the inoculant, the fading, and the solidification time. Additionally, the data of several thermal analysis, oxygen activity measurements and the final chemical analysis were included.Initial programme designs using machine learning algorithms based on neural networks achieved encouraging results. To improve the degree of accuracy, this algorithm was subsequently adapted and refined for the nodularity of graphite.
确定球墨铸铁中石墨球墨度的人工智能方法
在球墨铸铁的生产过程中,复杂的冶金相互关系会导致石墨的形成和局部微观组织的巨大差异。采用人工智能算法来描述受各种冶金参数影响的石墨形成过程。此外,石墨形态形成过程中复杂的物理关系也受到加工边界条件的控制,其影响在日常铸造操作中很难评估。相关输入参数的影响可以使用基于同时发生的条件和模式的人工智能来预先确定。通过预测局部石墨形成,确定了稳定生产的措施,从而提高了结构模拟的准确性。在此过程中,利用统计回归方法对从初装药到终铸的最重要的主导变量进行了整理和分析,以预测石墨球的球墨性。我们比较了线性回归、高斯过程回归、回归树、提升树、支持向量机、浅神经网络和深度神经网络的预测精度。作为输入参数,我们使用了生产过程的45个特征,包括装药的组成、过热时间、熔化容器的类型、孕育剂的类型、褪色和凝固时间等基本信息。此外,还包括了几次热分析、氧活度测量和最终化学分析的数据。使用基于神经网络的机器学习算法的初始程序设计取得了令人鼓舞的结果。为了提高精度,随后对该算法进行了调整和改进,以适应石墨的球墨性。
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