Deep learning LSTM for predicting thermally induced geometric errors using rotary axes’ powers as input parameters

Ngoc, Huy Vu; Mayer, Josef; Bitar-Nehme, Elie

doi:10.1016/j.cirpj.2021.12.009

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…Recurrent neural networks (RNNs) are robust deep neural networks with high performance that process serial data of varying lengths for end-to-end distribution [22]. Long short-term memory (LSTM) is an advanced RNN architecture that includes the memory unit proposed by Hocklet and Schmiduber to solve the gradient vanishing problem in RNNs.…”

Section: Lstmmentioning

confidence: 99%

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array

Zhang

et al. 2023

Sensors

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Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects.

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Section: Lstmmentioning

confidence: 99%

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array

Zhang

et al. 2023

Sensors

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“…Guo et al (2022) proposed convolutional neural network (CNN)-LSTM based on CNN and LSTM for thermal error modeling. Ngoc et al (2022) and Liu et al (2021aLiu et al ( , 2021b used LSTM for thermal error modeling. LSTM network has been applied more and more in the field of thermal error modeling and has more robust prediction accuracy than the traditional model used for thermal error modeling.…”

Section: Introductionmentioning

confidence: 99%

Thermal error modeling method of truss robot based on GA-LSTM

Li,

Chen,

2024

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Purpose The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability. Design/methodology/approach Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling. Findings By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy. Originality/value The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

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“…For example, Fujishima et al [ 13 ] proposed a novel deep-learning thermal error compensation method in which the compensation weight can be changed adaptively according to the reliability of thermal displacement prediction. The deep learning convolutional NN algorithm [ 14 ], bidirectional long short-term memory (LSTM) deep learning algorithm [ 15 ], and stacked LSTM algorithm [ 16 ] have all been used for thermal error modeling. Furthermore, several researchers have built hybrid thermal error models by combining different algorithms to take advantage of their respective features [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

Wei

Zhou

et al. 2023

Sensors

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Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

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Deep learning LSTM for predicting thermally induced geometric errors using rotary axes’ powers as input parameters

Cited by 18 publications

References 29 publications

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array

Thermal error modeling method of truss robot based on GA-LSTM

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

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