The modeling method on thermal expansion of CNC lathe headstock in vertical direction based on MOGA

Hou, Ruisheng; Du, Hongyang; Yan, Zongzhuo; Yu, Wenbin; Tao, Tao; Mei, Xuesong

doi:10.1007/s00170-019-03728-9

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Multivariate regression equations were developed using three critical temperature points, T6, T8, and T9, and the amount of spindle heat deflection in equations ( 4) to (9), where z is the z-axis compensation and x 1 , x 2 , and x 3 are the critical temperature points T6, T8, and T9, respectively. Table 3 shows the root mean square error indices after thermal deformation model training.…”

Section: Resultsmentioning

confidence: 99%

“…Second, the CSO-RBF neural network deals with the nonlinear relationship between temperature variables and thermal errors. Hou et al 9 proposed a multi-objective genetic algorithm, which will combine model-based and data-based, based on a physical model of thermal expansion in the vertical direction of the lathe spindle box with a model based on thermal run-in experimental data, high accuracy, and robustness, and the residuals of the model estimation are within 15 μm. Shi et al 10 proposed a thermal error modeling method based on a Bayesian neural network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using regression analysis and fuzzy inference

et al. 2023

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During the machining process, the computer numerical control machine is susceptible to variations in ambient temperature, cutting heat, and friction within the transmission parts, which generate different heat sources. These heat sources affect the machine structure in different ways, causing deformation of the machine and displacement of the tooltip and workpiece position, ultimately resulting in deviations in machining accuracy. The amount of thermal drift depends on several factors, including the material of the machine components, the cutting conditions, the duration of the machining process, and the environment. This study proposes a hybrid optimization algorithm to optimize the thermal variables of computer numerical control machine tool spindles. The proposed approach combines regression analysis and fuzzy inference to model the thermal behavior of the spindle. Spindle speed and 16 temperature measurement points distributed on the machine are input factors, while the spindle's axial thermal error is considered an output factor. This study develops a regression equation for each speed to account for the different temperature rise slopes and spindle thermal variations at different speeds. The experimental results show that the hybrid thermal displacement compensation framework proposed in this study effectively reduces the thermal displacement error caused by spindle temperature variation. Furthermore, the study finds that the model can be adapted to significant variations in environmental conditions by limiting the machining speed range, which significantly reduces the amount of data needed for model adaptation and shortens the adaptation time of the thermal displacement compensation model. As a result, this framework can indirectly improve product yield. The effects observed in this study are remarkable.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using regression analysis and fuzzy inference

et al. 2023

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“…Generally, the literature has only reported the establishment of thermal error models of single-machining-type lathes [5][6][7] and milling machines [8][9][10][11]. The workpiece is fixed on the spindle of a lathe and is processed using a tool along the rotating axis of the workpiece to produce rotary bodies, such as shafts and sleeves.…”

Section: Introductionmentioning

confidence: 99%

“…The workpiece is fixed on the spindle of a lathe and is processed using a tool along the rotating axis of the workpiece to produce rotary bodies, such as shafts and sleeves. Hou et al [5] used the multiobjective genetic algorithm (MOGA) to compensate for the vertical-direction thermal errors of the lathe tool tip. They combined the theoretical thermal deformation of the headstock in the vertical direction with the experimental thermal deformation of the main shaft.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a thermal compensation model for a turning–milling compound machine

Chuang,

Kuo,

Jhang

et al. 2024

Preprint

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Thermal errors are considered the most significant errors in precision machining. The heat sources of a turning– milling compound machine (TMCM) are diverse and complex. This study proposes a new thermal compensation model in a TMCM to compensate for thermal errors. First, the relationship between the tool tip deformation direction and the temperature increase trend is considered. The temperature points that have a pronounced influence on the tool tip deformation are selected from 76 measured temperature points. Temperature sensitive points are then identified through F-test feature engineering. The temperature increases and thermal deformation data of the temperature sensitive points under the characteristic and training conditions are used as the input and output in training a recurrent neural network model. When the recurrent neural network model was trained, the automatic parameter adjustment method of the RandomSearch in the Keras tuner toolbox was used. The minimum mean-square error (MSE) of the verified operating conditions was taken as the evaluation index, and a thermal compensation model was established. Compared with the average root-mean-square errors (RMSE) of the validation and test conditions along the six axes, the maximum difference in the average RMSE (only 2.12 μm) occurred in the direction of the spindle Y-axis. Noticeably, the thermal compensation model established in this study has high robustness and good universality. In addition, the average accuracy of the validation and testing conditions along the six axes improved by 84.3%. The proposed thermal compensation model is highly reliable and can effectively improve the thermal compensation accuracy.

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“…The radial basis function (RBF) and back propagation neural network were then used to establish the model, the results showed that the prediction accuracy of the RBF neural network was better than the traditional back propagation neural network. Hou et al [12] proposed a combination of a multi-objective genetic algorithm (MOGA) with an approximate physical model and spindle thermal performance test data to establish a thermal error prediction model. The results showed the model to be accurate and robust.…”

Section: Introductionmentioning

confidence: 99%

A Linear Regression Thermal Displacement Lathe Spindle Model

Lin

et al. 2020

Energies

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Thermal error is one of the main reasons for the loss of accuracy in lathe machining. In this study, a thermal deformation compensation model is presented that can reduce the influence of spindle thermal error on machining accuracy. The method used involves the collection of temperature data from the front and rear spindle bearings by means of embedded sensors in the bearing housings. Room temperature data were also collected as well as the thermal elongation of the main shaft. The data were used in a linear regression model to establish a robust model with strong predictive capability. Three methods were used: (1) Comsol was used for finite element analysis and the results were compared with actual measured temperatures. (2) This method involved the adjustment of the parameters of the linear regression model using the indicators of the coefficient of determination, root mean square error, mean square error, and mean absolute error, to find the best parameters for a spindle thermal displacement model. (3) The third method used system recognition to determine similarity to actual data by dividing the model into rise time and stable time. The rise time was controlled to explore the accuracy of prediction of the model at different intervals. The experimental results show that the actual measured temperatures were very close to those obtained in the Comsol analysis. The traditional model calculates prediction error values within single intervals, and so the model was divided to give rise time and stable time. The experimental results showed two error intervals, 19µm in the rise time and 15µm in the stable time, and these findings allowed the machining accuracy to be enhanced.

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The modeling method on thermal expansion of CNC lathe headstock in vertical direction based on MOGA

Cited by 10 publications

References 26 publications

Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using regression analysis and fuzzy inference

Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using regression analysis and fuzzy inference

Establishment of a thermal compensation model for a turning–milling compound machine

A Linear Regression Thermal Displacement Lathe Spindle Model

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