Influence of operating parameters of hydroabrasive cutting of various materials on the accuracy of observing the cutting width
Abstract
This article considers the issue of building regression models to determine the influence of operating parameters of hydroabrasive cutting of various materials on the accuracy of observing the cutting width. Input parameters include the amount of abrasive feed, the amount of pressure of cutting fluid (water) and the viscosity of the processed material, and the output parameter is the cutting width. For more precise control of the cutting width, another initial parameter is taken into account - the cutting width at the exit from the material. This parameter of hydroabrasive cutting is important to achieve the desired accuracy and quality of cutting. A preliminary analysis of possible neural network architectures for building regression models has been performed using Neuro Solutions software. The MLPR-2-B-L neural network with the Levenberg-Marquardt learning method has been selected according to analysis results. The application of this model makes it possible to obtain weighting factors that have been used to predict the initial value of the cutting width at the input of the cutting jet to the processed material and at the exit of the cutting jet from the processed material based on the input parameters. An assessment of errors, adequacy and informativeness of regression models has been carried out. The use of histograms of residuals makes it possible to analyze the obtained results. In addition, a program in the MathCAD environment, which allows to analyze the obtained results and predict the cutting width based on the created regression model both at the input of the cutting jet into the processed material and at the exit from the material, has been developed. The obtained results determine the importance of the input parameters of hydroabrasive cutting and demonstrate the possibility of using them to predict the cutting width. This work is of practical importance for industrial enterprises that use hydroabrasive cutting, because accurate predictions of the cutting width allow to optimize the cutting process, ensuring high quality and efficiency and accuracy of work
Keywords
hydroabrasive cutting; cutting width; regression model; abrasive nozzle wear; process controllability; processing accuracy
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