Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

Ye, Wenhua; Guo, Yanshuang; Zhou, Fei; Liang, Ruijun; Chen, Weifang

doi:10.1007/s40436-020-00293-3

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…It is known that u = 6,v=1. The number of hidden layer neurons is calculated between [4,13]. In order to find the most points of the hidden layer, we use Bayesian neural network to train different number of neurons.…”

Section: B Determine the Number Of Neurons In The Hidden Layermentioning

confidence: 99%

“…In addition, the thermal error model also needs to be considered for the same type of machine tools to further verify the robustness and practicability of the model. Ye et al [4] proposed a new regression analysis modeling method to determine the thermal deformation coefficient of machine tool moving shaft. The parameters of the thermal prediction model were obtained by the measurement of the thermal error, current boundary and machining conditions with sensors.…”

Section: Introductionmentioning

confidence: 99%

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Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

et al. 2021

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The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution. Thus, thermal error has become a key factor affecting system motion accuracy. To maximize the accuracy and efficiency of thermal error compensation for linear motor feed system, an improved modeling method for the thermal error of the linear motor feed system based on Bayesian neural networks is proposed in combination with the strong generalization performance and avoidance of overfitting of Bayesian neural networks. And the specific modeling ideas are as follows: Firstly, the X-Y cross-type two-axis linear motor feed system is as the test object. Aiming at the slow convergence , over fitting and under fitting problems of traditional neural network, Bayesian neural network is used to model the thermal error of linear motor feed system .Secondly, In order to avoid the influence of multicollinearity data on the final results, the grey relation analysis method is used to screen the temperature measuring points, and the data with large relation degree is selected for modeling to ensure the prediction accuracy of neural network. Thirdly, and the temperature variables of sensitive points and thermal positioning errors are taken as data input samples. Fourthly, a Bayesian neural network model is established. Fifthly, the hyperparameters of the Bayesian neural network is determined by a calculating method of Hessian matrix by Gauss Newton approximation. And finally, a thermal error prediction model is established. The comparison and analysis with the neural network constructed by ordinary Levenberg-Marquardt algorithm after a series of experimental demonstrations see that the prediction accuracy of the proposed method can be enhanced by up to 10%. It also shows that the prediction model has the advantages of high precision, strong generalization ability, strong anti-disturbance ability and strong robustness, etc. Therefore, the prediction model is expected to be widely used in the prediction and compensation of thermal error of the feed system of high-speed CNC machine tools in practical machining occasions.

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Section: B Determine the Number Of Neurons In The Hidden Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

et al. 2021

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“…Hu Shi [9] introduced a new model based on Bayesian neural networks, reducing the thermally induced errors of the CNC machine tool feed drive system from approximately 18.2 µm to 5.14 µm, resulting in a 71% error reduction. Wenhua Ye [10] presented a novel modeling method to determine the thermal deformation coefficients of machine tool dynamic axes. They used regression theory to establish a thermal error correction model for machine tool positioning and straightness, validating its accuracy on the QLM27100-5X experimental platform of a five-axis linked gantry machine tool sourced from Xiqiao Lian CNC Machine Tool Sales Co., Ltd., located in Wuxi, China.…”

Section: Introductionmentioning

confidence: 99%

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Fang,

Qian,

et al. 2024

Electronics

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Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.

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“…The feeding system's thermal error is reduced from 1.73 to 0.88 μm with the online compensation. In addition to the neural networks, the multiple linear regression [8,9] ， support vector machine(SVM) [10] and grey rough set theory [11] were also widely adopted in the feed system thermal error modeling [12] . Due to the difficulty of measuring the screw temperature distribution directly, the temperature at the nut and bearing where the data is easier to collect is taken as the input to the data-based model.…”

Section: Introductionmentioning

confidence: 99%

Transient thermal error modeling of a ball screw feed system

Zhao

et al. 2022

Int J Adv Manuf Technol

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The thermal behaviors of the ball screw feed system significantly affect the machining accuracy of the machine tool, which is widely valued by users and manufacturers. In the current ball screws thermal error models, the moving heat source at nuts is simplified to a fixed heat source to simplify the calculation, which can decrease the model's prediction accuracy. A novel thermal error modeling method for the ball screw feed system is proposed by this paper based on the FEM. In the proposed method, the moving heat source at the nut is loaded into the model by changing the grid distribution at different time steps. Meanwhile, a series of thermal experiments are carried out to collect the thermodynamic data with the machine tool's feed drive system operating at a different speed. And then the multi-objective optimization method is introduced to obtain the heat generated by the nuts and the convection coefficient between the air and the screw. The experimental results show that the thermal error of the ball screw feed system is different when the feed system runs in different intervals, even if the temperature rise at measuring points is the same. In the final, experiments under different conditions are carried out and the proposed model is accurate enough to predict the thermal error of the ball screw feed system.

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Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

Cited by 14 publications

References 19 publications

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Transient thermal error modeling of a ball screw feed system

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