APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO ENHANCE THE EFFICIENCY OF THE CUTTING METALWORKING PROCESS

<p><em><span>The given article discusses the application of artificial neural networks (ANNs) to enhance the efficiency of the cutting metalworking process. The main challenges related to selecting optimal processing conditions, diagnosing tool conditions and adaptive process control are outlined. A method for modeling cutting parameters based on a multilayer perceptron is presented, which allows for the prediction of cutting force and temperature in the cutting zone. The feasibility of using neural network algorithms for optimizing technological parameters, controlling tool wear and predictive maintenance is substantiated. Methods for optimizing neural network training algorithms, including network structure selection, adaptive weight adjustment, data normalization, and feature selection, are discussed. In the given research the application of these methods that enhances prediction accuracy, reduces training time, and lowers production costs are described as well. In the future, the integration of neural network technologies into CNC systems and adaptive control opens up new opportunities for automating metalworking processes.</span></em></p>