This paper researches on the tool wear condition monitoring by cutting sound signal and workpiece surface texture based on analysis of the relative situation.
本论文在分析现状的基础上，从切削声信号和工件表面纹理这两个方面对刀具磨损状态监测技术进行了研究。
参考来源 - 基于声音和图像的刀具磨损状态监测技术的研究·2,447,543篇论文数据，部分数据来源于NoteExpress
可听阈内的切削声信号包含着丰富的刀具磨损信息。
The cutting sound signal in audible range includes a plenty of tool-wear information.
找出刀具磨损信息在可听阈内的主要集中频段能提高切削声信号的信噪比，改善识别结果。
Finding out the major concentrated frequency band of tool-wear information can enhance SNR (Signal Noise Ratio) and improve tool-wear degree recognition result.
本论文在分析现状的基础上，从切削声信号和工件表面纹理这两个方面对刀具磨损状态监测技术进行了研究。
This paper researches on the tool wear condition monitoring by cutting sound signal and workpiece surface texture based on analysis of the relative situation.
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