Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability

Remaining useful life prediction is essential for cutting tool utilization evaluation and replacement decision-making. However, it is very difficult to build a mechanism model for the time-varying and non-linear cutting tool wear and life decreasing process. Based on big samples, artificial intelligence–based models have weak interpretability and un-quantized uncertainty. In fact, the cutting tool degradation is a stochastic process, and cutting tool remaining useful life is a random variable. Then, a non-linear Wiener-based cutting tool wear and remaining useful life prediction model is proposed for a specific cutting tool. The probability density function of remaining useful life is derived to quantize uncertainty. On the basis of Bayesian model, unknown parameters are estimated and updated by using history data and real-time data, respectively. Measurement variability is also considered to improve the accuracy and reliability. Experimental study verifies the approach’s effectiveness and accuracy. Detailed comparisons validate the approach’s advantages over existing models. By quantizing the uncertainty of cutting tool remaining useful life prediction with confidence intervals, the model is meaningful for cutting tool selection and replacement decision-making.