Kosarac Aleksandar, Cep Robert, Trochta Miroslav, Knezev Milos, Zivkovic Aleksandar, Mladjenovic Cvijetin, Antic Aco
Faculty of Mechanical Engineering, University of East Sarajevo, 71123 Istocno Sarajevo, Bosnia and Herzegovina.
Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70833 Ostrava, Czech Republic.
Materials (Basel). 2022 Nov 4;15(21):7782. doi: 10.3390/ma15217782.
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.
本文介绍了使用小型输入输出数据集来预测高速电动主轴热行为的神经网络模型的开发与评估。使用当前最流行的深度学习框架之一Keras开发并评估了不同的神经多输出回归模型。开发并评估人工神经网络时考虑了以下因素：拓扑结构（内部隐藏层和神经元数量）的影响、学习参数以及验证技术。使用网络完全未知的数据集对神经网络进行了模拟。人工神经网络模型用于分析工作条件对电动磨床主轴热行为的影响。人工神经网络模型对主轴热行为的预测准确率在95%至98%之间。结果表明，具有小型数据集 的人工神经网络模型能够准确预测不同工作条件下主轴的温度。此外，分析表明冷却液类型对主轴单元温度有非常强烈的影响，特别是对于水的强化冷却。
