A spindle thermal error modeling based on 1DCNN-GRU-Attention architecture under controlled ambient temperature and active cooling

Jia, Guangjie; Zhang, Xu; Wang, Xingzhao; Zhang, Xianpeng; Huang, Nuodi

doi:10.1007/s00170-023-11616-6

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Luo et al [19] introduced a single-dimensional convolutional minimal gate unit thermal error forecasting model built upon residuals, which incorporated both cloud computing and edge computing, and the final accuracy reached 98.18%. Jia et al [20] developed a 1DCNN-GRU-attention architecture model and finally proved experimentally that the model's correctness in forecasting thermal errors reached 81.53% in the complex case of multiple coupling. Du et al [21] put forth a model grounded in nonlinear programming and thermal distortion decoupling and compared it with MLR and BPNN thermal deformation prediction models, ultimately showcasing the superior performance of their proposed model.…”

Section: Introductionmentioning

confidence: 99%

A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM

Gao,

Xia,

Guo

2023

Applied Sciences

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Given motorized spindles’ extensive periods of prolonged high-velocity operation, they are prone to temperature changes, which leads to the problem of thermal error, leading to diminished precision in machining operations. To address the thermal error issue in motorized spindles of computer numerical control (CNC) machine tools, this study proposes a pelican optimization algorithm (POA)-optimized convolutional neural network (CNN)–long short-term memory (LSTM) hybrid neural network model (POA-CNN-LSTMNN). Initially, the identification of temperature-sensitive locations in the spindle system is performed using a combination of hierarchical clustering, the K-medoids algorithm, and Pearson’s coefficient calculation. Subsequently, the temperature data from these identified points, along with real-time collected spindle thermal error data, are employed to construct the model. The Pelican optimization algorithm is used to enhance the model parameters to achieve the best performance. Finally, the proposed model is subjected to a comparative analysis with other thermal error prediction models. Drawing from the experimental findings, it is evident that the POA-CNN-LSTMNN model exhibits superior prediction performance.

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Section: Introductionmentioning

confidence: 99%

A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM

Gao,

Xia,

Guo

2023

Applied Sciences

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“…This enables the diagnostic model to handle various types of complex data. The basic structure of a 1DCNN is illustrated in Figure 1, consisting primarily of an input layer, convolutional layers, pooling layers, fully connected layers, and an output layer [28,29].…”

Section: One-dimensional Convolutional Neural Networkmentioning

confidence: 99%

Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet

Xu,

Sui,

et al. 2024

Processes

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To address the issues of uneven sample lengths in the centrifuge machine bearings of the ternary precursor, inaccurate fault feature extraction, and insensitivity of important feature channels in rolling bearings, a rolling bearing fault diagnosis method based on adaptive sample length adjustment of one-dimensional convolutional neural network (1DCNN) and squeeze-and-excitation network (SeNet) is proposed. Firstly, by controlling the cumulative variance contribution rate in the principal component analysis algorithm, adaptive adjustment of sample length is achieved, reducing data with uneven sample lengths to the same dimensionality for various classes. Then, the 1DCNN extracts local features from bearing signals through one-dimensional convolution-pooling operations, while the SeNet network introduces a channel attention mechanism which can adaptively adjust the importance between different channels. Finally, the 1DCNN-SeNet model is compared with four classic models through experimental analysis on the CWRU bearing dataset. The experimental results indicate that the proposed method exhibits high diagnostic accuracy in rolling bearings, demonstrating good adaptability and generalization capabilities.

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“…The experiment verifies that the model has higher prediction accuracy than the traditional model and solves the problem of temperature-sensitive point selection in thermal-error modeling. Jia et al [ 5 ] constructed a thermal-error prediction model using a one-dimensional convolutional neural network-gated recurrent unit (1DCNN-GRU-Attention). The convolution module is used to replace the traditional temperature-sensitive point selection method.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Sensing of Thermal Error of CNC Machine Tool Spindle Based on Multi-Source Information Fusion

Yang,

Liu,

Zhang

et al. 2024

Sensors

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Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment’s results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems.

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A spindle thermal error modeling based on 1DCNN-GRU-Attention architecture under controlled ambient temperature and active cooling

Cited by 9 publications

References 32 publications

A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM

A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM

Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet

Intelligent Sensing of Thermal Error of CNC Machine Tool Spindle Based on Multi-Source Information Fusion

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