The motivation of the presented paper is the desire to create a universal tool to analyse the process of austenite decomposition during the cooling process of various steel grades. The presented analysis concerns the application of Recurrent Artificial Neural Networks (RANN) of the Long Short-Term Memory (LSTM) type for the analysis of the transition path of the cooling curve. This type of network was selected due to its ability to predict events in time sequences. The proposed generalisation allows for the determination of the austenite transformation during the continuous cooling process for various cooling curves. As training data for the neural network, values determined from the macroscopic model based on the analysis of Continuous Cooling Transformation (CCT) diagrams were used. All relations and analyses used to build training/testing or validation sets are presented in the paper. The modelling with the use of LSTM network gives the possibility to determine the incremental changes of phase transformation (in a given time step) with the assumed changes of temperature resulting from the considered cooling rate.
The work is devoted to the issue of calculating material removal during magnetic abrasive processing. Cutting grains have random dimensional characteristics, are randomly located on the surface of the tool, the workpiece has an irregular profile. The cutting parts of the grain tops partially remove the chips, and partially elastically-plastic deform the metal. Part of the vertices falls into the risks on the surface of the workpiece formed by the previous machining, and part -into the risks from passing through the previous vertices. This process is determined by the probability of the contact of the top of the grain with the metal. The developed stochastic models make it possible to predict the removal of metal from the treated surface depending on the time and parameters of the operation, which creates the prerequisites for their use in the design of polishing operations.
The work is devoted to the problem of calculating the surface roughness during magnetic abrasive processing. Cutting grains have random dimensional characteristics, are randomly located on the surface of the tool, the workpiece has an irregular profile. The cutting parts of the grains partially remove the chips and partially elastoplastically deform the metal. Some of the vertices fall into the marks on the surface of the workpiece formed by the previous processing, and some - on the marks from the passage of the previous vertices. This process is determined by the probability of contact of the top of the grain with the metal. The developed probabilistic-theoretical model makes it possible to predict the removal of metal from the treated surface depending on the time and parameters of the operation, which creates the prerequisites for their use in the design of polishing operations.
The article is devoted to the distribution of the radii of the cutting tips during magnetic-abrasive processing and comparison of distribution polygons. The conducted experimental researches are stated. Graphs of the distribution of the radii of the cutting vertices are given.
The article is devoted to the distribution of the radii of the cutting tips during magnetic-abrasive processing and comparison of distribution polygons. The conducted experimental researches are stated. Graphs of the distribution of the radii of the cutting vertices are given.
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