In this study, the vibration transmitted solely from a spindle to the worktable is proposed to be a crucial feature of wear prediction models for machine tools. To validate the effectiveness of the proposed feature, a feature ranking and screening methodology was also used for developing a tool wear prediction model. First, the features extracted from vibration signals were ranked according to their contributions to tool wear prediction. The features were then filtered through a screening process based on singular value decomposition to eliminate redundant features, which exhibited collinearity with features of higher rankings. The aim of the aforementioned steps was to use a relatively small number of highly appropriate features to create an accurate real-time tool wear prediction model. The results indicated that the accuracy of the tool wear prediction model based on the proposed feature ranking and screening methodology is higher than that of models without feature ranking or screening. Moreover, the proposed feature was proven to be more important and effective than other features.

在这项研究中，仅从主轴传递到工作台的振动被认为是机床磨损预测模型的关键特征。为了验证所提出特征的有效性，特征分级和筛选方法还用于开发刀具磨损预测模型。首先，根据振动信号中提取的特征对工具磨损预测的贡献进行排名。然后通过基于奇异值分解的筛选过程对特征进行过滤，以消除多余的特征，这些冗余的特征与较高等级的特征表现出共线性。前述步骤的目的是使用相对少量的高度合适的特征来创建准确的实时工具磨损预测模型。结果表明，基于提出的特征分级和筛选方法的刀具磨损预测模型的准确性高于没有特征分级或筛选的模型。此外，事实证明，所提出的功能比其他功能更重要和有效。