In machining processes, various phenomena occur during cutting operation. These phenomena can disturb the production through the reduction of part quality and accuracy. An easy way to control the process is by monitoring incontrollable parameters, such as generated temperature and vibration. The acquired vibration signals can provide information regarding tool life, surface roughness, cutting performances, and workpiece defects. This paper evaluates the possibility of monitoring the tool life during the turning process of AISI 1045 steel using laser Doppler vibrometer (LDV); the surface roughness has been measured along with the tool wear until reaching its limit value of 300μm. Furthermore, this paper also outlines the application of CEEMDAN technique to process the acquired signals for the monitoring processes. RMS and SCI indicators have been used to describe the wear progress, then, the artificial neural network has been adopted to achieve a real-time wear monitoring. The obtained results show that the CEEMDAN helps for isolating tool vibration signature. The RMS indicator does not provide enough information about the wear behavior; however, good results have been achieved by SCI indicator. The ANNs fed by SCI deliver accurate results allowing for real-time wear monitoring.

在机械加工过程中，切削操作过程中会出现各种现象。这些现象会通过降低零件质量和精度来干扰生产。控制过程的一种简单方法是监控不可控参数，例如产生的温度和振动。获取的振动信号可以提供有关刀具寿命、表面粗糙度、切削性能和工件缺陷的信息。本文评估了使用激光多普勒测振仪（LDV）监测AISI 1045钢车削过程中刀具寿命的可能性；表面粗糙度随刀具磨损一起测量，直至达到其极限值 300μm。此外，本文还概述了 CEEMDAN 技术在处理监测过程中获取的信号的应用。使用RMS和SCI指标描述磨损进程，然后采用人工神经网络实现实时磨损监测。获得的结果表明，CEEMDAN 有助于隔离工具振动特征。RMS 指示器没有提供关于磨损行为的足够信息；然而，SCI指标取得了良好的效果。SCI 提供的人工神经网络提供准确的结果，允许实时磨损监控。