Novel thermal error modeling method for machining centers

Zhu, Xiaolong; Xiang, Sitong; Yang, Jing

doi:10.1177/0954406214545661

Cited by 14 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each constant speed (n), there is a maximum thermal deformation (Z ss ). Thus, parameters a and b are determined by fitting the linear function in equation (5). Similarly, τ g and τ d are determined by the average value of all experiments.…”

Section: Improved Exponential Modelmentioning

confidence: 99%

“…2 The data-driven model is considered as a learning machine that establishes the relationship between temperature and thermal error, such as multiple linear regression, artificial neural network (ANN), and support vector machine. 3 In recent years, modified back propagation ANN, 4 adaptive vector quantization network, 5 back propagation ANN with genetic algorithm, 6 least squares support vector machine, 7 radial basic function ANN, 8 and generalized regression ANN 9 are proposed to improve the accuracy and robustness of the prediction results. Furthermore, principal component analysis 10 and ridge regression algorithm 11 are used to eliminate the collinearity of temperature data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Liao

Yin

Xie

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

Establishing a robust thermal error model is crucial for thermal error compensation of machine tools. In this paper, an improved exponential model for thermal error modeling was proposed. First, the improved exponential model is established by combining the exponential model and the temperature-dependent model. Second, the improved fruit fly optimization algorithm is used to determine the parameters. Finally, two strategies are used to select the optimal sensor location from six potential ambient temperature sensors. The ambient temperature data is proved to be effective in improving the robustness of the model. The average root mean square error of the proposed model, the exponential model without ambient temperature, and the linear model are 1.18 μm, 1.79 μm, and 2.70 μm, respectively. The modeling results show that the proposed model has high accuracy and strong robustness, which is suitable for workshop environment and variable conditions in thermal error modeling of machine tools.

show abstract

Section: Improved Exponential Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Liao

Yin

Xie

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…Dya et al proposed an improved ENN based on world cup optimization (WCO) and fluid search optimization (FSO) algorithms [21]. Zhu et al used the output hidden feedback ENN to establish the effective thermal error model of the machining center [22]. Shao et al proposed a modeling method based on an improved ENN, in which neurons in the hidden layer are activated by a set of Chebyshev orthogonal basis functions [23].…”

Section: Introductionmentioning

confidence: 99%

Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Zhang

Zhao

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Dynamic neural networks (DNNs) are widely used in data‐driven modeling of nonlinear control systems. Due to the complexity of the actual operating nonlinear power systems, rigorous dynamic models are always unknown. DNNs can focus on methods that only use input and output information to establish accurate dynamic models and reduce noise in measured data, which is called data‐driven modeling. The core of the DNN is the feedback with memory function. This paper analyzes the traditional Elman neural network (ENN) and nonlinear auto‐regression with exogenous input (NARX) neural network with different structure feedback structures, and proposes a full feedback dynamic neural network (FF‐DNN) with exogenous input. Eight different kinds of neural networks (including ENN, NARX, etc.) are compared and analyzed. The eight kinds of neural networks are applied on the experimental data of the DC‐AC inverter and the power system of Zhejiang Juchuang Smart Technology Company Park in Wenzhou. The experimental results are used to compare the performance of the data‐driven models established by eight different kinds of neural networks under different noise conditions, verify the robustness and generalization performance of dynamic data‐driven modeling based on FF‐DNN, and demonstrate the feasibility and effectiveness of FF‐DNN in actual power systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

show abstract

“…Zhu [21] introduced the BP neural network to establish a new effective thermal error model for machining centers. The validity of the method is validated by an experiment in a machining center.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

et al. 2022

View full text Add to dashboard Cite

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

show abstract

Novel thermal error modeling method for machining centers

Cited by 14 publications

References 20 publications

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

Contact Info

Product

Resources

About