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

Aleksandar Kosarac; Robert Cep; Miroslav Trochta; Milos Knezev; Aleksandar Zivkovic; Cvijetin Mladjenovic; Aco Antic

doi:10.3390/ma15217782

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

Materials (Basel). 2022 Nov 4;15(21):7782. doi: 10.3390/ma15217782.

Authors

Aleksandar Kosarac¹, Robert Cep², Miroslav Trochta², Milos Knezev³, Aleksandar Zivkovic³, Cvijetin Mladjenovic³, Aco Antic³

Affiliations

¹ Faculty of Mechanical Engineering, University of East Sarajevo, 71123 Istocno Sarajevo, Bosnia and Herzegovina.
² Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70833 Ostrava, Czech Republic.
³ Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.

Abstract

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.

Keywords: Keras; TensorFlow; deep learning; high-speed motorized spindle; neural network; small dataset; thermal behavior.

Grants and funding

The paper presents part of the research results of the project “Collaborative systems in the digital industrial environment” No. 142-451-2671/2021-01/02, supported by the Provincial Secretariat for Higher Education and Scientific Research of the Autonomous Province of Vojvodina.