Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network
Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network
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DOI：
10.1016/j.asoc.2015.09.034
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发表时间：
2016-01-01
影响因子：
8.7
通讯作者：
Cicek, Adem
中科院分区：
文献类型：
--
作者：
Kara, Fuat;Aslantas, Kubilay;Cicek, Adem
提出了一种基于人工神经网络的AISI 316L不锈钢正交车削实验切削温度预测方法。对切削力进行了实验和数值分析，得到了切削温度。为此，使用涂层（TiCN + Al 2 O3 + TiN和Al 2 O3）和未涂层硬质合金刀片进行切削试验。使用Deform-2D程序进行数值建模，并使用Johnson-Cook（J-C）材料模型。涂层和未涂层刀具的数值切削力与实验结果进行了比较。另一方面，对每个切削刀具的切削温度值进行了数值计算。采用人工神经网络模型，通过数值切削力预测数值切削温度。在切削温度预测中，采用隐层七神经元的网络结构和LM学习算法获得了最好的结果。最后，通过将实验切削力输入到由人工神经网络得到的公式中，预测实验切削温度。统计结果（R-2、RMSE、MEP）令人满意。这表明所建立的神经网络模型是一个强大的预测实验切削温度。(C)2015爱思唯尔B. V.保留所有权利。
In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al2O3 + TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson-Cook (J-C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R-2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures. (C) 2015 Elsevier B.V. All rights reserved.