中圖分類(lèi)號(hào)：TP181 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234208 中文引用格式： 劉雪杰,，李國(guó)富，任潞. 基于主軸電機(jī)電流信號(hào)的表面粗糙度檢測(cè)[J]. 電子技術(shù)應(yīng)用,，2024,，50(2)：54-59. 英文引用格式： Liu Xuejie，Li Guofu,，Ren Lu. Surface roughness detection based on spindle motor current signal[J]. Application of Electronic Technique,，2024，50(2)：54-59.
Surface roughness detection based on spindle motor current signal
Liu Xuejie,，Li Guofu,，Ren Lu
College of Mechanical Engineering and Mechanics，Ningbo University,，Ningbo 315211,， China
Abstract： Workpiece waste is usually caused by delayed detection of surface roughness. A rapid surface roughness detection classification based on the current signal of the spindle motor is proposed for the first time. The current signals of the spindle motor under different surface roughness processing conditions are collected through experiments, and the current signals are decomposed into different frequency bands through wavelet packet decomposition. The current signals of different frequency bands are evaluated by the energy characteristics and the margin factors, and the low correlation frequency bands are filtered. Then the features are screened through random forest to reduce the redundancy of features. The total harmonic distortion feature achieves built-up edge detection during the machining process. The workpiece surface roughness detection accuracy is as high as 95%. And the detection time is within 2 seconds. Spindle current signal analysis basically achieves fast and accurate detection of workpiece surface roughness.
Key words : spindle motor current signal,；wavelet packet decomposition；random forest,；the total harmonic distortion,；surface roughness
