Accurate remaining useful life prediction is meaningful for cutting tool usability evaluation. Over the years, experience-based models, data-driven models, and physics-based models have been used individually to predict cutting tool remaining useful lives. In order to improve prediction performances, different prognostics models can be combined to leverage their advantages. In this paper, a hybrid cutting tool remaining useful life prediction approach is proposed by combining a data-driven model and a physics-based model. By using force, vibration and acoustic emission signals, the data-driven model monitors cutting tool wear conditions based on empirical mode decomposition and back propagation neural network. On the basis of the Wiener process, the physics-based model builds a cutting tool condition degradation model to predict cutting tool remaining useful lives. Experimental study verifies the approach's effectiveness, accuracy, and robustness. Then, cutting tool remaining useful lives can be predicted more accurately during the machining process.

In order to analyse product–service performance (PSP) during product–service systems' (PSSs) running time, a PSP evaluation method is proposed. Interrelationship between a PSS's provider and accepter is defined as product–service relationship (PSR). Concepts of product–service network and product–service chain are put forward to describe interrelationship among PSRs from different perspectives. The concept of PSP is proposed to evaluate interrelationship among PSR. Detailed evaluation indexes are designed to measure the five factors of PSP, including time, quality, cost, stability and reliability. A case study is provided to test the validity and efficiency of the evaluation model of PSP. The analysis results show that the evaluation model can make good efforts in benefiting product–service platform's running, monitoring and optimising.