Development of multi-objective optimization models for electrochemical machining process
Development of multi-objective optimization models for electrochemical machining process
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DOI：
10.1007/s00170-007-1204-8
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发表时间：
2008-10-01
影响因子：
3.4
通讯作者：
Santhi, M.
中科院分区：
文献类型：
--
作者：
Asokan, P.;Kumar, R. Ravi;Santhi, M.
由于电化学加工（ECM）的复杂性，确定最佳切削参数以提高切削性能非常困难。因此，优化操作参数是加工的重要步骤，特别是对于非常规的加工程序，如ECM。ECM加工参数的选择在很大程度上取决于操作者的技术和经验，因为它们的范围众多且多样化。机床制造商提供的加工参数不能满足操作者的要求。由于对于特定作业的任意所需加工时间，它们不能提供最佳条件。针对这一问题，提出了多元回归模型和人工神经网络模型作为确定电解加工中最优加工参数的有效方法。本文以电流、电压、流量和间隙为加工参数，以金属去除率和表面粗糙度为目标。然后运用灰色关联分析方法，计算多目标模型的灰色等级。建立了多元回归模型和人工神经网络模型来映射工艺参数与目标之间的等级关系。实验数据分为训练数据和测试数据。找到预测等级，然后计算每个模型的实验等级与预测等级之间的百分比偏差。线性回归模型、对数变换模型（不包括交互项）和人工神经网络模型训练数据的平均百分比偏差分别为12.7、25.6和3.03。三种模型测试数据的平均百分比偏差分别为9.83、26.8和2.67。在检验三个模型的平均百分比偏差时，人工神经网络的百分比偏差较小。因此，人工神经网络被认为是最好的预测模型。根据人工神经网络的测试结果，对运行参数进行了优化。最后，利用方差分析对多元回归模型和人工神经网络模型进行显著性分析。
Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator's technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator's requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.