Abstract
Due to the large size of the heavy duty machine tool-foundation systems, space temperature difference is high related to thermal error, which affects to system’s accuracy greatly. The recent highly focused deep learning technology could be an alternative in thermal error prediction. In this paper, a thermal prediction model based on a self-organizing deep neural network (DNN) is developed to facilitate accurate-based training for thermal error modeling of heavy-duty machine tool-foundation systems. The proposed model is improved in two ways. Firstly, a dropout self-organizing mechanism for unsupervised training is developed to prevent co-adaptation of the feature detectors. In addition, a regularization enhanced transfer function is proposed to further reduce the less important weights of the process and improve the network feature extraction capability and generalization ability. Furthermore, temperature sensors are used to acquire temperature data from the heavy-duty machine tool and concrete foundation. In this way, sample data of thermal error predictive model are repeatedly collected from the same locations at different times. Finally, accuracy of the thermal error prediction model was validated by thermal error experiments, thus laying the foundation for subsequent studies on thermal error compensation.
Highlights
Environmental temperature has an enormous influence on large machine tools with regards to thermal error, which is different from the effect on ordinary-sized machine tools [1]
Deep neural network is one of the most commonly used tools in regression and prediction, and we propose that it could be a potential alternative to solve the particular problem in our domain
The process is divided into two steps: First, the self-organizing deep neural network (DNN) is expanded into a forward network and the data signal is transmitted to the input layer, to the layer, and so on until the output layer; second, the backpropagation error is generated for fine tuning the neural network parameters
Summary
Environmental temperature has an enormous influence on large machine tools with regards to thermal error, which is different from the effect on ordinary-sized machine tools [1]. Two additional factors should be taken into account: (1) due to variation in the ambient environment and working conditions, thermal error of the concrete foundation should not be neglected [18,19]; (2) in order to build accurate thermal error prediction model, it is necessary to build a neural network model with strong network feature extraction ability, generalization ability and network stability To address these challenges, this paper proposes a self-organizing deep neural network (DNN) to improve the feature extraction capability and generalization ability of unsupervised training and to solve the problems of overfitting and lengthy training times. Based on the above model, the thermal error of the heavy-duty machine tool -foundation systems are predicted accurately
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