Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized.

Thin−walled parts are widely used in many important fields because of performance and structural lightweight requirements. They are critical parts because they usually carry the core functions of high−end equipment. However, their high−performance machining has been facing severe challenges, among which the dynamics problem is one of the most important obstacles. The machining system is easily subjected to chatter due to the weak rigidity of the thin−walled structure and slender cutting tool, which significantly deteriorates the surface quality and reduces the machining efficiency. Extensive studies aiming at eliminating machining chatter have been carried out in the recent decades. This paper systematically reviews previous studies on the identification of system dynamic characteristics, modeling and prediction of chatter stability, and chatter elimination/suppression methods and devices. Finally, existing problems are summarized, and future research is concluded.