Accurate monitoring and prediction of tool wear conditions have an important influence on the cutting performance, thereby improving the machining precision of the workpiece and reducing the production cost. However, traditional methods cannot easily achieve exact supervision in real time because of the complexity and time-varying nature of the cutting process. A method based on Digital Twin (DT), which establish a symmetrical virtual tool system matching exactly the actual tool system, is presented herein to realize high precision in monitoring and predicting tool wear. Firstly, the framework of the cutting tool system DT is designed, and the components and operations rationale of the framework are detailed. Secondly, the key enabling technologies of the framework are elaborated. In terms of the cutting mechanism, a virtual cutting tool model is built to simulate the cutting process. The modifications and data fusion of the model are carried out to keep the symmetry between physical and virtual systems. Tool wear classification and prediction are presented based on the hybrid-driven method. With the technologies, the physical–virtual symmetry of the DT model is achieved to mapping the real-time status of tool wear accurately. Finally, a case study of the turning process is presented to verify the feasibility of the framework.

The severe work hardening phenomenon generated in the machining of Inconel 718 is harmful to continue cutting processes, while being good for the component’s service performance. This paper investigates the performance of cryogenic assisted machining used in the cutting processes, which can reduce the waste of fluids. The influence of dry and cryogenic machining conditions with different cutting speeds on the work hardening layer is investigated based on the interrupted cutting of Inconel 718. Cutting temperature distribution obtained from simulations under different conditions is used to discuss the potential mechanism of work hardening. Then, the depth of work hardening and degree of work hardening (DWH) are investigated to analyze the surface deformation behavior, which strengthens the machined surface during metal cutting processes. From the cutting experiments, the depth of the work hardening layer can reach more than 60 μm under the given cutting conditions. In addition, a deeper zone can be obtained by the cooling of liquid nitrogen, which may potentially enhance the wear performance of the component. The results obtained from this work can be utilized to effectively control the work hardening layer beneath the surface, which can be applied to improve the service performance.

The manufacturing of miniaturized components is indispensable in modern industries, where the uncut chip thickness (UCT) inevitably falls into a comparable magnitude with the tool edge radius. Under such circumstances, the ploughing phenomenon between workpiece and tool becomes predominant, followed by the notable formation of dead metal zone (DMZ) and piled-up chip. Although extensive models have been developed, the critical material flow status in such microscale is still confusing and controversial. In this study, a novel material separation model is proposed for the demonstration of workpiece flow mechanism around the tool edge radius. First, four critical positions of workpiece material separation are determined, including three points characterizing the DMZ pattern and one inside considered as stagnation point. The normal and shear stresses as well as friction factors along the entire contact region are clarified based on slip-line theory. It is found that the friction coefficient varies symmetrically about the stagnation point inside DMZ and remains constant for the rest. Then, an analytical force prediction model is developed with Johnson–Cook constitutive model, involving calibrated functions of chip-tool contact length and cutting temperature. The assumed tribology condition and morphologies of material separation including DMZ are clearly observed and verified through various finite element (FE) simulations. Finally, comparisons of cutting forces from cutting experiments and predicted results are adopted for the validation of the predictive model.

Purpose: In practical workshop production process, there are many production emergencies, e.g. new manufacturing tasks, facilities failure and tasks change. On one hand, it results in poor timeliness and reliability of real-time production data collection, acquisition and transmission; on the other hand, it increases the difficulty of real-time data tracking and monitoring. This paper aims to develop a novel RFID-based tracking and monitoring approach of real-time data in production workshop (TM_rfid) to solve them. Design/methodology/approach: At first, a three-layer model of real-time data based on RFID has been constructed, which contains RFID-based integrated acquisition center; “RFID & Cloud-service-rules”-based calculation and analysis center; and “RFID & Ontology-knowledge-base”-based monitoring and scheduling center. Then, a targeted analysis and evaluation method of TM_rfid with feasibility, quality and performance has been proposed. Finally, a prototype platform of a textile machinery manufacturing enterprise has been built to verify the effective of TM_rfid. Findings: The effectiveness of TM_rfid is verified by applying two groups of actual experimental data from the case enterprise, the results show that TM_rfid can promote the efficiency, reliability and feasibility of tacking and monitoring of real-time data in production workshop. Originality/value: RFID-based tracking and monitoring approach of real-time data in production workshop has been developed to solve the data information transmission and sharing problem. Three analysis and evaluation approaches have been introduced to solve the un-standardized evaluation problem of RFID application. A prototype platform of a textile machinery manufacturing enterprise has been constructed.