Abstract:Nickel-based superalloy is a kind of metal material that is widely used to manufacture hightemperature parts in the fields of aviation and aerospace, but it is also a typical difficult-tocutting material. The precision cutting of nickel-based superalloy has always been an important manufacturing problem. Based on the tests of conventional drilling with three kinds of twist drills, the machinability of Inconel 718 was evaluated comprehensively by drilling force, tool wear and machining quality, and the cutting … Show more
In vibration-assisted drilling, the wear state of the drill bit affects the processing quality of the hole. The traditional method of identifying the wear state of the drill bit adopts the method of packet decomposition, ignoring the timing characteristics of the signal. In this paper, the force and acoustic emission signals in vibration-assisted drilling are used. The Gram angle field converts the onedimensional time series into a two-dimensional image, while retaining the trajectory of the time series in the high-dimensional space. Based on the Graham difference field (GADF) image of force and AE, the Inception improved convolutional neural network (IN-CNN) is used to identify the wear state. The experiment proves that compared with the traditional convolutional neural network, BP neural network and support vector machine, the recognition rate of IN-CNN drill wear state based on GADF is 93.1 %, which is increased by 2.5 %, 10.6 % and 8.1 % respectively. It provides a reliable condition monitoring method for the state identification of the drill bit in semi-closed vibration-assisted machining, and has practical engineering significance for improving the machining accuracy and efficiency of composite equal-holes.
In vibration-assisted drilling, the wear state of the drill bit affects the processing quality of the hole. The traditional method of identifying the wear state of the drill bit adopts the method of packet decomposition, ignoring the timing characteristics of the signal. In this paper, the force and acoustic emission signals in vibration-assisted drilling are used. The Gram angle field converts the onedimensional time series into a two-dimensional image, while retaining the trajectory of the time series in the high-dimensional space. Based on the Graham difference field (GADF) image of force and AE, the Inception improved convolutional neural network (IN-CNN) is used to identify the wear state. The experiment proves that compared with the traditional convolutional neural network, BP neural network and support vector machine, the recognition rate of IN-CNN drill wear state based on GADF is 93.1 %, which is increased by 2.5 %, 10.6 % and 8.1 % respectively. It provides a reliable condition monitoring method for the state identification of the drill bit in semi-closed vibration-assisted machining, and has practical engineering significance for improving the machining accuracy and efficiency of composite equal-holes.
Testing of machinability of the UMCo50 superalloy was carried out within the project following the actual production of the semi-finished product by casting. Turning was chosen as the machining method to minimize the effect of an interrupted cut. Considering the machinability of a hard-to-machine alloy, the cutting material with the fine-grained WC-Co carbide with the TiN/TiAlN gradient PVD coating was selected. The progression of cutting forces, chip formation and tool wear were evaluated. Images of the material structure of the semi-finished product and the resulting chips were taken. From the measured values, graphs of the dependence of the chip thickness ratio on the cutting speed and the Taylor´s dependence of the tool durability on the cutting speed were obtained. The aim of the experiment was selection and verification of suitable cutting conditions for efficient machining of this superalloy, especially the appropriate value of the cutting speed. The recommended value of the cutting speed was 50-70 m.min -1 , while the tangential component of the cutting force was in the values usual for corrosionresistant steels.
Over the last decades, nickel-based superalloys with TBC coatings have been used as the main material for hot section turbine parts. The next step in the development of engines and increasing the combustion temperature is the use of Ceramic Matrix Composites (CMC). Nevertheless, in the presence of water vapour or molten salts, accelerated degradation of substrate material. These problems can be prevented by additional layers or coatings produced on its surface, or combinations of layers and coatings that form Environmental Barrier Coatings (EBCs). The aim of the research was the preparation of samples of a mixture of erbium oxide powders with silicon oxide with the addition of: polyvinyl alcohol, starch and cellulose gum. Then their technological properties were examined. A mixture with the most favourable properties was selected and sprayed using HV-APS method using with various process parameters and investigated. Conducted research showed that energy of HV-APS process is too low for synthesis of erbium disilicate in the resulting coating. The material was only melted, not vaporized. Making powder agglomerates with an average size of 150 µm with the addition of 3% PVA leads to a significant decrease in the surface area of powder grains. This results in a significant increase in flowability and allows it to be used as a charge material for APS plasma spraying.
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