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首页> 外文期刊>International Journal of Production Research >Optimization of Cutting Parameters in Face Milling with Neural Networks and Taguchi based on Cutting Force, Surface Roughness and Temperatures
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Optimization of Cutting Parameters in Face Milling with Neural Networks and Taguchi based on Cutting Force, Surface Roughness and Temperatures

机译：基于切削力，表面粗糙度和温度的神经网络和田口铣削中的切削参数优化

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摘要

Prediction of cutting parameters as a function of cutting force, surface roughness and cutting temperature is very important in face milling operations. In the present study, the effect of cutting parameters on the mentioned responses were investigated by using artificial neural networks (ANN) which were trained by using experimental results obtained from Taguchi's L8 orthogonal design. The experimental results are compared with the results predicted by ANN and the Taguchi method. By training the ANN with the results of experiments which are corresponding with the Taguchi L8 design, with only eight experiments an effective ANN model is trained. By using this network model the other combinations of experiments which did not perform previously, could be predicted with acceptable error.
机译：在端面铣削操作中，根据切削力，表面粗糙度和切削温度来预测切削参数非常重要。在本研究中，使用人工神经网络（ANN）研究了切削参数对上述响应的影响，该人工神经网络是使用田口L8正交设计获得的实验结果进行训练的。将实验结果与ANN和Taguchi方法预测的结果进行比较。通过用与田口L8设计相对应的实验结果训练ANN，仅用8个实验就可以训练出有效的ANN模型。通过使用该网络模型，可以以可接受的误差预测以前未执行的其他实验组合。

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