Proper understanding of the nature of the process of forecasting, modeling and research methods of wood drying is necessary to enable saving time and material resources in developing real industrial projects in order to be implemented on the domestic and foreign markets. The object of our investigation is the aerodynamic processes in wood drying chambers that wiil be used for calculating the parameters of agent timber (ASD) using SolidWorks Flow Simulation software application. To perform the tasks we used software for automatic calculation and design of components for the wood drying chamber using the application-programming interface, which is called SolidWorks API. The primary objective in this work is to determine the correct choice points, which will be used to form the arrays of input for the training samples. To perform this task, we decided to divide the wood drying chamber into five planes, each of which is defined by twenty points. Firstly, we have created a radial-basis artificial neural network to get the value of any parameter for drying agent. The function of the neural network of radial basis has three layers. As a result of using the ‘Mynetwork' function, we have got the desired value parameter of drying agent in wood drying chamber at a given point and in a given time. Then, we proceed with the graphical representation of the results. To perform this task it was decided to create a graph, where you can track the temperature change of agent timber in space and time. Our space is presented by previously mentioned points that are placed along the perimeter of the drying wood chamber according to the coordinates, at the total number of 100 points. Therefore, to construct the graph of the appearance, we must specify two axes. To conclude, as the result of the work using SolidWorks Flow Simulation experiments, values of drying agent parameters, which include relative humidity, air velocity and its temperature were found. With the average value, graphs for parameter changes of drying agent by time were constructed. Another equally important task that was done was the creation of radial-basis neuron network, which would allow us to get values for parameters of drying agent anywhere in the chamber drying wood and at any time. Lastly, we have created the graphs for drying agent parameter. This task was also performed in the Matlab environment.
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