Neural Networks For Diagnostics Of Metal Cutting Machine Modules

DOI

10.2991/itids-19.2019.18How to use a DOI?

Keywords

cutting machining, operational diagnostic, long short term memory, deep neural networks

Abstract

The work is devoted to solving the problem online diagnostics of machine tools modules using data-based models. The authors propose a diagnostic method that includes models based on long short-term neural memory networks as a repository of frequency reference values. Data for training neural networks is a frequency spectrum reflecting the oscillations of the tool and the workpiece normal to surfaces caused by the presence of a manufacturing defect in the module element of a metalworking machine. Neural network model with long short-term memory are used for approximation the nonlinear frequency characteristics. For classification of module defects proposed a second neural network that compare the neural network model of the reference spectrum with the spectrum obtained from the actual quality parameters of the part in real time, determine the sources of defects. To evaluate the effectiveness of the method, a series of experiments were carried out with the definition of defective machine modules. An experimental result of the application of proposed method is given.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kamil Masalimov
AU  - Rustem Munasypov
PY  - 2019/05
DA  - 2019/05
TI  - Neural Networks For Diagnostics Of Metal Cutting Machine Modules
BT  - Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)
PB  - Atlantis Press
SP  - 95
EP  - 100
SN  - 1951-6851
UR  - https://doi.org/10.2991/itids-19.2019.18
DO  - 10.2991/itids-19.2019.18
ID  - Masalimov2019/05
ER  -