轴承加工质量保证预测的LR和NN方法比较

Kang-Hsien Tu, Chun-Chieh Wang, J. Ho, Hui-Chun Tseng
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引用次数: 0

摘要

刀具的状态是轴承切削过程中的一个重要因素。提出了几种在预测误差情况下监测刀具和观察轴承套圈质量的方法。本文介绍了逻辑回归(LR)和神经网络(NN)方法在刀具寿命监测中的应用。我们在两个模型中共输入88个数据,误差分别为6.30%和3.16%。结果表明,神经网络能很好地适应刀具工况,明显优于逻辑回归方法。因此,可以得出结论,神经网络在轴承切割面积预测中的未来应用是非常令人鼓舞的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The comparison between LR and NN methods for quality assurance prediction of bearing machining
The state of a cutting tool is an important factor in bearing cutting process. Several methods developed for monitoring cutting tool and observing the bearing ring quality while prediction error has been attempted. This paper presents logistic regression (LR) and neural network (NN) methods that have been employed in tool life monitoring. We input 88 data total into two models, and resulted in errors of 6.30% and 3.16%, respectively. The results showed that NN is well-suited to the cutting tool condition, obviously which NN method is better than that of logistic regression. Thus, it can be concluded that NN is quite encouraging for future applications in the prediction of bearing cutting area.
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