Diagnostics of 3D Printing on a CNC Machine by Machine Learning

Diagnostics and dynamic optimization of the electrical arc in 3D printing on a CNC machine are considered. The applicability of nonlinear dynamic methods in assessing the stability of 3D printing is assessed. Artificial neural networks are used in classifying and optimizing the process parameters.

Saved in:
Bibliographic Details
Published in:Russian engineering research 2021, Vol.41 (4), p.320-324
Main Authors: Kabaldin, Yu. G., Shatagin, D. A., Anosov, M. S., Kolchin, P. V., Kiselev, A. V.
Format: Article
Language:English
Subjects:
3-D printers
Artificial neural networks
Dynamic stability
Engineering
Engineering Design
Machine learning
Nonlinear dynamics
Optimization
Process parameters
Stability analysis
Three dimensional printing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diagnostics and dynamic optimization of the electrical arc in 3D printing on a CNC machine are considered. The applicability of nonlinear dynamic methods in assessing the stability of 3D printing is assessed. Artificial neural networks are used in classifying and optimizing the process parameters.
ISSN:1068-798X
1934-8088
DOI:10.3103/S1068798X21040109