用人工神经网络模型研究aisi4340 MQL车削条件对表面粗糙度的影响

P. Powar
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引用次数: 0

摘要

工业中切削液的大量使用正在报告与员工健康和环境污染有关的问题,这促进了最小量润滑(MQL)。研究报告了在en24最小润滑量下,利用人工神经网络(ANN)建立了考虑切削环境、切削条件影响的数控车削表面粗糙度模型。利用输入输出数据集、人工神经网络模型、训练神经网络的重复次数、速率、隐藏节点和训练函数对模型进行优化。分析采用MATLAB下多层前馈结构的神经网络分析。最后,在训练完成后,对人工神经网络进行测试,以评估其预测和泛化性能。通过应用一个新的输入数据集来测试人工神经网络,该数据集不包括在训练过程中。这种基准测试使用了众所周知的统计工具决定系数。通过考虑相关系数(R)来评价人工神经网络的充分性。
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Investigations into Effect of Cutting Conditions On Surface Roughness Under MQL Turning of AISI 4340 by ANN Models
Enormous usage of cutting fluid in industries is reporting the issues related to health of employee, environmental pollution which promotes the Minimum Quantity Lubrication (MQL). The study reports surface roughness modelling in CNC turning under minimum quantity lubrication of EN 24, taking into account effect of cutting environment, cutting conditions using artificial neural networks (ANN). The optimisation of models are processed using the input-output data sets, ANN model, the repetitions in training the neural network, rate, hidden nodes and the training function. ANN analysis with multilayer feed forward structure under MATLAB is adopted in the analysis. Finally, After the training, the ANN is tested in order to evaluate its predictive and generalization performances. Testing the ANN is carried out by applying a new input data set, which was not included in the training process. The well known statistical tools coefficient of determination is used for this benchmarking. The adequacy of the ANN is evaluated by considering the coefficient of correlation (R).
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来源期刊
Journal of Mines, Metals and Fuels
Journal of Mines, Metals and Fuels Energy-Fuel Technology
CiteScore
0.20
自引率
0.00%
发文量
101
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