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Modeling and critical analysis of material removal rate in WEDM of Oil Hardening Non Shrinking Die Steel (OHNS)

机译:石油硬化模具钢水钢(OHNS)内部材料去除率建模及批判性分析(OHNS)

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Wire EDM is a complicated machining process that is used for producing complex 2D and 3D shapes. In this work, the process parameters associated with the wire electrical discharge machining (WEDM) of oil hardening non-shrinkage (OHNS) die steel material were investigated through response surface method (RSM) and an artificial neural network (ANN). A quadratic model developed through RSM was used to predict material removal rate (MRR) with appreciable precision. The various input variables, viz. pulse on time (PON), pulse off time (POFF), wire feed rate (WFR) and input current (I), have been considered. A comparison between the predicted and measured values of MRR was performed for each experiment. It was noted that the RSM predicted values are in a 95%confidence interval. Statistical analysis shows the capabilities of the developed models to predict the MRR more accurately. Also, ANN model estimates MRR with high precision compared using the RSM model. Support vector regression (SVR) is also used to analyze the impact of various process parameters. The results show that all approaches are strongly capable of predicting the response. Analysis the WEDM is a very effective. Of the three approaches ANN is superior.
机译:电线EDM是一种复杂的加工过程,用于制造复杂的2D和3D形状。在这项工作中,通过响应面法(RSM)和人工神经网络(ANN)研究了与油硬化非收缩(OHNS)模钢材料的线放电加工(HODM)相关的工艺参数。通过RSM开发的二次模型用于预测具有可观精度的材料去除率(MRR)。各种输入变量,viz。已经考虑了脉冲接通时间(PON),脉冲关闭时间(POFF),导线进给速率(WFR)和输入电流(I)。对每个实验进行MRR的预测和测量值之间的比较。有人指出,RSM预测值处于95%的置信区间。统计分析显示了开发模型的能力更准确地预测MRR。此外,Ann模型使用RSM模型比较了高精度的MRR。支持向量回归(SVR)还用于分析各种过程参数的影响。结果表明,所有方法都能够预测响应。分析WEDM是非常有效的。在三种方法中,安为优越。

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