Forecasting the quality of technological processes by methods of artificial neural networks
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2025.3.09Keywords:
accuracy, details, quality, forecasting, machine learning, neural network, technological processAbstract
A set of models of feed-forward neural networks has been created to obtain operational forecasts of the quality of mechanical engineering processes. It is established that the use of the Back Propagation of Error machine learning algorithm allows for obtaining forecasted estimates for the controlled parameter of the metalworking process with significantly smaller ranges of the mean absolute percentage error, mean square error, relative approximation error, and variance ratio criterion compared to the BFGS algorithm. It is shown that the proposed MLP neural network models can be recommended for practical applications in controlling the accuracy of the machining process of shaft-type parts.
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