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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Tool wear monitoring in milling of titanium alloy Ti-6Al-4 V under MQL conditions based on a new tool wear categorization method
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Tool wear monitoring in milling of titanium alloy Ti-6Al-4 V under MQL conditions based on a new tool wear categorization method

机译：基于新工具磨损分类方法，在MQL条件下磨削钛合金TI-6AL-4 V磨削的工具磨损监测

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摘要

Tool wear monitoring is crucial during machining of difficult-to-cut materials to save cost and improve efficiency. In this paper, a tool wear-monitoring strategy was proposed for milling of titanium alloy Ti-6Al-4 V under inner minimum quantity lubrication (MQL) conditions. Unlike the usual categorization method, tool wear was categorized into four states based on tool wear mechanism, tool wear rate, and tool life. Thus, more detailed information of tool could be predicted for tool wear monitoring. Cutting forces and acoustic emission were measured online as raw datasets. Statistical features were extracted from time and frequency domain, and mutual information (MI) was used for feature selection. Then, linear discriminant analysis (LDA) was adopted for dimensionality reduction and finding the optimal datasets for training. At last, nu-Support vector machine (nu-SVM) was applied for training and prediction. The proposed strategy had a prediction accuracy of 98.9%, which could be considered as valid and useful for tool wear monitoring.
机译：工具磨损监测在加工难以切割的材料时至关重要，以节省成本，提高效率。本文提出了一种工具磨损监测策略，用于在内部最小量润滑（MQL）条件下研磨钛合金Ti-6Al-4V。与通常的分类方法不同，刀具磨损基于工具磨损机制，工具磨损率和工具寿命分为四个状态。因此，可以预测工具磨损监测的更详细信息。切割力和声发射在线测量为原始数据集。从时间和频域中提取统计特征，互信息（MI）用于特征选择。然后，采用线性判别分析（LDA）以进行维度减少，并找到最佳数据集进行培训。最后，申请了Nu-Support向量机（Nu-SVM）进行培训和预测。拟议的策略具有98.9％的预测准确性，可被视为有效且有用的工具磨损监测。

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