Remaining useful life prediction of milling tool under segmented variable machining conditions considering the...

Under the multi-variety and small-batch production mode, the machining parameters switch segments throughout the tool life cycle. This segmented parameter switching leads to frequent jumps in degradation indicators at the transition points between segments. Furthermore, existing methods rely on a single degradation indicator for degradation modeling, which is insufficient to comprehensively reflect the tool degradation process. These issues significantly impede accurate tool remaining useful life (RUL) prediction under segmented variable machining conditions. Therefore, a method based on condition-adaptive degradation indicator and binary Wiener process is proposed for milling tool RUL prediction. The method initially extracts two highly characteristic degradation indicators from multi-source sensing signals. Subsequently, the effect of segmented variable machining parameters on these indicators is eliminated through a baseline transformation algorithm. Then, a nonlinear binary Wiener process with random effects is developed to depict the correlation between the degradation indicators. Besides, the Copula function is employed to model the dependence between the marginal RUL distributions of the two degradation indicators, thereby predicting the RUL under the coupling of degradation indicators. Finally, a segmented variable-condition milling experiment is carried out to validate the proposed method.