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首页> 外文期刊>International Journal of Machine Tools & Manufacture: Design, research and application >Predictions on surface finish in electrical discharge machining based upon neural network models
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Predictions on surface finish in electrical discharge machining based upon neural network models

机译:基于神经网络模型的放电加工表面光洁度预测

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摘要

Predictions on the surface finish of work-pieces in electrical discharge machining (EDM) based upon physical or empirical models have been reported in the past years. However, when the change of electrode polarity has been considered, very few models have given reliable predictions. In this study, the comparisons on predictions of surface finish for various work materials with the change of electrode polarity based upon six different neural-networks models and a neuro-fuzzy network model have been illustrated. The neural-network models are the Logistic Sigmoid Multi-layered Perceptron (LOGMLP), the Hyperbolic Tangent Sigmoid Multi-layered Perceptron (TANMLP), the Fast Error Back-propagation Hyperbolic Tangent Multi-layered Perceptron (Error TANMLP), the Radial Basis Function Networks (RBFN), the Adaptive Hyperbolic Tangent Sigmoid Multi-layered Perceptron, and the Adaptive Radial Basis Function Networks. The neuro-fuzzy network is the Adaptive Neuro-Fuzzy Inference System (ANFIS). Being trained by experimental data initially screened by the Design of Experiment (DOE) method, the parameters of the above models have been optimally determined for predictions. Based upon the conclusive results from the comparisons on checking errors among these prediction models, the TANMLP, RBFN, Adaptive RBFN, and ANFIS model have shown consistent results. Also, it is concluded that the further experimental results have agreed to the predictions based upon the above four models.
机译:在过去的几年中,已经报道了基于物理或经验模型对放电加工(EDM)中工件表面光洁度的预测。但是,当考虑到电极极性的变化时,很少有模型给出可靠的预测。在这项研究中,说明了基于六种不同的神经网络模型和神经模糊网络模型对各种工作材料随电极极性变化而进行的表面光洁度预测的比较。神经网络模型是Logistic Sigmoid多层感知器(LOGMLP),双曲正切Sigmoid多层感知器(TANMLP),快速误差反向传播双曲正切多层感知器(Error TANMLP),径向基函数网络(RBFN),自适应双曲正切Sigmoid多层感知器和自适应径向基函数网络。神经模糊网络是自适应神经模糊推理系统(ANFIS)。通过最初由实验设计(DOE)方法筛选的实验数据进行训练,上述模型的参数已针对预测进行了最佳确定。根据这些预测模型之间检查错误的比较得出的结论性结果,TANMLP,RBFN,自适应RBFN和ANFIS模型显示出一致的结果。另外,可以得出结论,进一步的实验结果已经与基于上述四个模型的预测相吻合。

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