Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture

Liang, Yuchen; Li, Weidong; Lou, Ping; Hu, J. M.

doi:10.1016/j.jmsy.2020.10.008

Cited by 44 publications

(7 citation statements)

References 45 publications

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“…Zimmermann et al designed a new self-adaptive approach for thermal error prediction [4]. Liang et al developed an LSTM-based thermal error prediction model for heavy-duty CNCMT [5]. Li et al reviewed LSTM thermal error modeling methods for MTs [6].…”

Section: Related Work 21 Lstm-based Thermal Error Predictionmentioning

confidence: 99%

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Lu,

Zhu,

Wang

et al. 2023

Lubricants

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Traditional methods for predicting thermal error ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of thermal errors and affecting the normal processing of the CNC machine tool (CNCMT) spindle. To solve the above problem, we propose a thermal error prediction approach based on digital twins and long short-term memory (DT-LSTM). DT-LSTM combines the high simulation capabilities of DT and the strong data processing capabilities of LSTM. Firstly, we develop a DT system for the thermal characteristics analysis of a spindle. When the DT system is implemented, we can obtain the theoretical value of thermal error. Then, the experimental data is used to train LSTM. The output of LSTM is the actual value of thermal error. Finally, the particle swarm optimization (PSO) algorithm fuses the theoretical values of DT with the actual values of LSTM. The case study demonstrates that DT-LSTM has a higher accuracy than the single method by nearly 11%, which improves the prediction performance and robustness of thermal error.

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Section: Related Work 21 Lstm-based Thermal Error Predictionmentioning

confidence: 99%

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Lu,

Zhu,

Wang

et al. 2023

Lubricants

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“…Moreover, some scholars have employed recurrent neural network (RNN) containing time-series properties [18], to predict thermal error by fully considering the influence of thermal hysteresis effects [19,20]. However, given the problem of gradient disappearance or explosion during the backpropagation of RNN [21], researchers have widely used LSTM nerual network for thermal error prediction [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Axial Attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions

Sun,

Liu,

Qiu

et al. 2023

Preprint

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Spindle thermal error is a major factor affecting the machining accuracy of machine tools. The time-consuming experiments required to model the thermal error of each machine tool spindle makes exhaustive studies difficult. Therefore, it is critically important to develop a transferable spindle thermal error prediction model that ensures robustness and accuracy, which provides theoretical guidance for the thermal error compensation and accuracy improvement of different machine tools. To achieve this, this paper proposes a migratory prediction method based on spatial-temporal axial attention bidirectional long short-term memory (Axial Attention-BiLSTM) network to predict the thermal error of computer numerical control (CNC) machine tool spindles under varying working conditions. By analyzing the mechanism of spindle thermal error generation, the spindle system is divided into multiple temperature regions, and the importance of each region is automatically determined by using the Spatial Attention mechanism. At the same time, considering the historical dependence of thermal error, BiLSTM is used to fuse the previous and following time series information and determine the weights of different time steps by Temporal Attention mechanism to strengthen the times series memory of thermal error prediction. An Axial Attention-BiLSTM model for thermal error prediction is built based on the spindle test bench. Compared with the BiLSTM and long short-term memory (LSTM) models, this model shows better and more stable prediction performance when migrated to various working conditions of horizontal CNC grinding machines.

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“…(2) The LSTM model can only predict the short-term data rather than long-term. Moreover, under limited data, the accuracy of the prediction results will also decrease with the increase of the prediction period 18 . (3) The Forget Gate in the standard LSTM model is easy to ignore and exclude relevant contents in long sequence tasks.…”

Section: Introductionmentioning

confidence: 99%

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Zheng

Wang

et al. 2021

Sci Rep

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Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean $${R}^{2}$$ R 2 of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

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Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture

Cited by 44 publications

References 45 publications

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

An Axial Attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

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