Abstract
The article discusses a method for diagnosing and optimizing the dynamic state of an electric arc during 3D printing of workpieces from cold-resistant materials on a CNC machine within the framework of a cyber-physical system. The possibility of applying nonlinear dynamics methods to assess the stability of the 3D printing process and artificial neural processes for classifying and optimizing the parameters of the dynamic state of the 3D printing process is shown. Experimental studies of the cold resistance obtained by 3D printing of 09G2S steel samples were carried out taking into account the choice of optimal modes.
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