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Comparative Characteristics of Ductile Iron and Austempered Ductile Iron Modeled by Neural Network

机译:神经网络建模的球墨铸铁和奥氏体球墨铸铁的比较特性

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摘要

Experimental research of cutting force components during dry face milling operations are presented in the paper. The study was provided when milling of ductile cast iron alloyed with copper and its austempered ductile iron after the proper austempering process. In the study, virtual instrumentation designed for cutting forces components monitoring was used. During the research, orthogonal cutting forces components versus time were monitored and relationship of cutting forces components versus speed, feed and depth of cut were determined by artificial neural network and response surface methodology. An analysis was made regarding the consistency of the measured cutting forces and the values obtained from the model supported by an artificial neural network for the investigated interval of the cutting regime. Based on the results, an analysis of the feasibility of the application of austempered ductile iron in the industrial sector with the aspect of machinability as well as the application of the models based on artificial intelligence, was given. At the end of the presentation, the influence of the aforementioned cutting regimes on cutting force components is presented as well.
机译:本文介绍了干面铣削过程中切削力分量的实验研究。该研究是在经过适当的回火处理后铣削与铜及其奥氏体球墨铸铁合金化的球墨铸铁时进行的。在这项研究中,使用了设计用于切削力分量监控的虚拟仪器。在研究过程中,通过人工神经网络和响应面方法,监测了正交切削力分量与时间的关系,并确定了切削力分量与速度,进给量和切削深度的关系。对测量的切削力的一致性以及从人工神经网络支持的模型中获得的值进行了分析,以研究切削状态。根据结果​​,从可加工性的角度分析了奥氏体球墨铸铁在工业领域中应用的可行性以及基于人工智能的模型的应用。在介绍的最后,还介绍了上述切削方式对切削力分量的影响。

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