Multi-condition identification in milling Ti-6Al-4V thin-walled parts based on sensor fusion

•The proposed method can simultaneously identify chatter and tool wear failure coupled condition.•The complexity of multi-sensor features is reduced by upgraded principal component analysis algorithm.•The identification accuracy of multi-condition can be improved by a new extracted signal feature.•The experimental efficiency is improved by the design of process parameters.•The identification accuracy of the proposed method is much higher than that of traditional methods. Chatter and tool wear always coexist during the milling of Ti-6Al-4V thin-walled parts, while the boundary characteristics, time-varying, modal coupling, and position dependence characteristics in milling thin-walled parts lead to the variable spatial time–frequency distribution of the signal features related to milling conditions. As a result, the existing milling monitoring methods can only identify single chatter or tool wear while ignoring another condition. Therefore, in order to identify the chatter and tool wear simultaneously, a multi-condition identification method based on sensor fusion is proposed by fusing multi-source heterogeneous data with different spatial time–frequency distributions. Multi-sensor features of the milling process are extracted from sound, acceleration, and cutting bending moment signals. To reduce the complexity of the original feature dataset, an upgraded principal component analysis (UPCA) algorithm is proposed by screening sensor features in specific frequency bands. Moreover, a new signal feature considering energy proportion is extracted to improve the identification accuracy of multiple conditions. The process parameters are designed according to the stability lobe diagram (SLD) to improve the experimental efficiency. The experimental results show that the proposed method can identify the multi-milling condition composed of chatter and tool wear failure. With the help of UPCA and energy proportion, the computational efficiency is also improved, and the identification accuracy of the proposed method reaches 93.75%, which is much higher than the accuracy of classifiers with traditional data processing methods.