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Optimization of multiple-machining criteria in electrochemical machining of aluminum composites using design of experiments

机译:实验设计优化铝复合材料电化学加工中的多加工准则

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摘要

Electrochemical Machining (ECM) appears to be a promising non-traditional machining process to produce parts from difficult-to-machine materials with complex profiles. In ECM the machining criteria like metal removal rate (MRR), surface roughness (SR) and radial over cut (ROC) depends on machining parameters such as applied voltage, feed rate, electrolyte concentration, workpiece electrical conductivity etc. In composites, the electrical conductivity depends on the reinforcement content in the matrix. The salient feature of the present work is that the reinforcement content is considered as one of the machining parameter along with voltage, feed rate and electrolyte concentration and varied within the selected range for studying the effect of these parameters on the machining characteristics of electrochemical machining of LM6 Al/B_4C composites. Non-linear regression models have been formulated after conducting the experiments using the concept of central composite design of experiments and the developed models are tested with the help of twenty test cases. The quality of the machined surfaces is studied by using scanning electron microscopic (SEM) images. The responses MRR, SR and ROC are optimized individually and simultaneously based on response surface methodology (RSM). The average absolute percentage error in prediction of all responses for the non-linear model is found to be equal to 9.657. Moreover, the optimal values of MRR, SR and ROC are seen to be equal to 0.778 g/min, 3.923 μm and 0.673 mm respectively.
机译:电化学加工(ECM)似乎是一种有前途的非传统加工工艺,可以用难以加工的具有复杂轮廓的材料生产零件。在ECM中,加工标准(例如金属去除率(MRR),表面粗糙度(SR)和径向切削(ROC))取决于加工参数,例如施加的电压,进给速度,电解质浓度,工件电导率等。在复合材料中,电电导率取决于基体中钢筋的含量。本工作的突出特点是,将钢筋含量与电压,进给速度和电解质浓度一起作为加工参数之一,并在选定的范围内变化,以研究这些参数对电化学加工性能的影响。 LM6 Al / B_4C复合材料。在进行实验后,使用中央综合设计实验的概念制定了非线性回归模型,并在二十个测试用例的帮助下测试了开发的模型。通过使用扫描电子显微镜(SEM)图像研究了加工表面的质量。响应MRR,SR和ROC分别根据响应面方法(RSM)进行了优化。发现非线性模型的所有响应的预测中的平均绝对百分比误差等于9.657。此外,MRR,SR和ROC的最佳值分别等于0.778 g / min,3.923μm和0.673 mm。

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