Abstract
<jats:p>The objective of this study is to propose a methodology for selecting the architecture and training parameters of artificial neural networks that can be effectively applied to the enhancement of manufacturing processes. The research focuses on the utilization of neural network models in three key areas: optimization of machining parameters, quality assessment of finished products, and prediction of machine failures. The study examines selected neural network architectures, including multilayer perceptrons, recurrent LSTM networks, and convolutional neural networks. A detailed methodology was developed to determine suitable architectures and training parameters, encompassing a comprehensive process of data preparation, model selection, and adaptation to the specific requirements of individual industrial applications. A key part of this method was taking into account the types of data that were available and the specifics of the tasks. This let the best neural network architectures be chosen for each of the domains that were being studied.</jats:p>