TL;DR: In this paper, a mathematical model of the rate of flank wear is proposed, which takes into account the wear-accelerating effect of both the technological parameters of cutting and the temperature developing on the tool flank.
TL;DR: A sensor-based data-driven scheme using a deep learning tool and the similarity-based curve matching technique to estimate the RUL of a system, which demonstrates the competitiveness of the proposed method used for RUL estimation of systems.
TL;DR: Investigation of the influence of the CAD/CAM tool-material couple on tool wear and surface roughness after milling found that tool lifetime calculated by volume of milled material removed should be the measure provided by CAD-CAM manufacturers instead of a number of blocks.
Abstract: Statement of problem Computer-aided design/computer-aided manufacturing (CAD/CAM) machining influences the surface roughness of dental restorations and tool wear. Roughness must be suitable to meet clinical requirements, and the tool must last as long as possible. Purpose The purpose of this pilot study was to investigate the influence of the CAD/CAM tool-material couple on tool wear and surface roughness after milling. Material and methods Three tools (Lyra conical tool O1 mm; GACD SASU, Lyra conical tool O1.05 mm; GACD SASU, and Cerec cylinder pointed tool 12S; Sirona Dental Systems GmbH) and 3 CAD/CAM materials (Lava Ultimate; 3M ESPE, Mark II; VITA Zahnfabrik H. Rauter GmbH, and Enamic; VITA Zahnfabrik H. Rauter GmbH) were tested. The tool wear of 6 tool-material couples at a feed rate of 2 m/min was analyzed before and after 8 minutes of flank and climb milling with optical and scanning electron microscopy (SEM) observations and tool weighing. The surface roughness after milling was observed for 9 tool-material couples for flank and climb milling. Feed rates of 1, 2, 3, and 4.8 m/min were used for each couple. Ra, Rt, Rz, Sa, Sq, and Sz roughness criteria were measured. A paired comparison of tool-material couples was conducted with the Kruskal-Wallis test. Results The Mark II material led to more severe tool wear. Milling of Lava Ultimate resulted in chip deposits on the tool grit. The Cerec cylinder pointed tool 12S was less worn for each material tested. The Cerec cylinder pointed tool 12S and the Lyra conical tool O1.05 mm provided similar roughness measurements for the 3 materials tested. The Lyra conical tool O1.05 mm tool provided better roughness than the Lyra conical tool O1 mm tool for the Enamic material. Conclusion Tool lifetime calculated by volume of milled material removed should be the measure provided by CAD/CAM manufacturers instead of a number of blocks. This tool lifetime should be provided for the milling conditions associated with the material milled. Material hardness and tool grit are key factors for achieving a given roughness.
TL;DR: In this paper, an FEM-based approach was proposed to predict the rate of flank wear evolution for uncoated cemented carbide tools in longitudinal turning processes, which combines the concept of experimental design and Response Surface Methodology (RSM) with Finite Element (FE) modelling of the cutting process, allowing for a fairly accurate tool wear prediction with a significantly lower computational cost compared to other available numerical methods.
TL;DR: In this article, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner, which shows significant improvement in tool wear state estimation, reducing the prediction errors by almost half.
Abstract: An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fusion framework is an extension to the regularized particle filtering technique, used to facilitate updating the state prediction with a numerical inference model, when measurement models alone are not satisfactory. The results show significant improvement in tool wear state estimation, reducing the prediction errors by almost half, compared to the prediction model and sensor-based monitoring method used independently.
TL;DR: Experimental results show that ON-LSTM network achieved the best accuracy of short-term and long-term prediction, and it has the best robustness and convergence speed and it can be effectively applied to the RUL prediction of gears.