The method of self-learning based online tool wear monitoring in semi-finishing or finishing working step

An adaptive learning method for tool wear monitoring, taking the time of one machining process step as a monitoring period, is proposed, which aims to provide new ideas for tool life research and improve the reliability of machine tool operation. First, the motor current signal is collected as the original signal, and the RMS (root mean square) value of the current signal is extracted as the characteristic quantity. Statistical analysis of characteristic quantity RMS using SPSS (Statistical Product and Service Solutions) method shows that the RMS value of current signal obeys normal distribution approximately in the monitoring period with process step as the unit. Then, a monitoring method based on +/- 3 sigma principle of normal distribution is proposed. Although RMS does not follow normal distribution completely, it is still possible to estimate the dispersion range of RMS by introducing distribution coefficient K through +/- 3 sigma principle of statistical mathematics. Finally, a self-learning algorithm for the boundary mathematical model of tool wear monitoring is presented. According to the analysis of tool wear, the tool failure time could be calculated, and the tool life can be predicted. The experimental results show that the monitoring model can be formed quickly during semi-finishing and finishing machining and can get the satisfactory monitoring results.