Open AccessIntelligent diagnostics and control of 3D printing processes by electric arc surfacing of workpieces made of cold-resistant materials on a CNC machine using machine learning approaches and neuromorphic calculations
Author(s) -
Д. А. Шатагин,
Yu. G. Kabaldin,
М. С. Аносов,
Pavel Colchin
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/971/2/022001
Subject(s) - process (computing) , neuromorphic engineering , electric arc , state (computer science) , mechanical engineering , arc (geometry) , computer science , 3d printing , artificial neural network , engineering , control engineering , engineering drawing , artificial intelligence , algorithm , electrode , chemistry , operating system
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.