Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250735" target="_blank" >RIV/61989100:27230/22:10250735 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000883507500001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000883507500001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma15217782" target="_blank" >10.3390/ma15217782</a>

Result language

angličtina

Original language name

Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles

Original language description

This paper presents the development and evaluation of neural network models using a small input-output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques. The neural network was simulated using a dataset that was completely unknown to the network. The ANN model was used for analyzing the effect of working conditions on the thermal behavior of the motorized grinder spindle. The prediction accuracy of the ANN model for the spindle thermal behavior ranged from 95% to 98%. The results show that the ANN model with small datasets can accurately predict the temperature of the spindle under different working conditions. In addition, the analysis showed a very strong effect of type coolant on spindle unit temperature, particularly for intensive cooling with water.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20300 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Materials

ISSN

1996-1944

e-ISSN

1996-1944

Volume of the periodical

15

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

nestrankovano

UT code for WoS article

000883507500001

EID of the result in the Scopus database

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