Prediction of surface roughness of particle board using neural networks

The article is devoted to the study of the features of predicting the surface roughness of particle boards using neural networks. It is noted that the surface roughness of particle boards depends on many factors, the author analyzes these factors and highlights the differences between traditional methods of roughness prediction and the use of a neural network. Special attention in the article is paid to the study of the advantages of using neural network forecasting of chipboard roughness. In conclusion, the author concludes that the use of neural networks to predict the surface roughness of particle boards can be a useful tool for board manufacturers, helping them to control product quality and improve production processes. Roughness forecasting will reduce the number of defects and improve the overall product quality, which will lead to resource savings and increased competitiveness in the market.