应用人工神经网络预测车削过程中的切削力

M. Ibraheem
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引用次数: 5

摘要

切削力是决定机床使用性能和产品质量的重要因素。在车削加工中,进给速度、切削深度和刀具噪声半径等因素影响着表面粗糙度和切削力。采用人工神经网络模型对切削速度(m/min)、进给速度(mm/rev)、切削深度(mm)和工件硬度(Map)等输入进行切削力预测。该神经网络模型的输出为加工后的切削力参数,神经网络显示出加工力各分量的全部(输出)切削力FT (N)、进给力FA (N)和径向力FR (N)与实验数据完全吻合。使用了25个实验数据样本,其中19个用于训练网络。此外,还进行了6个实验测试来测试该网络。研究表明,神经网络是一种可靠、精确的数控车削加工参数预测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.
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