Machine Tool Thermal Error Research Articles

Self-optimizing thermal error compensation models with adaptive inputs using Group-LASSO for ARX-models

Thermal errors have a significant impact on the machining accuracy of five-axis machine tools. The thermal adaptive learning control (TALC), which combines adaptable data-based models and on-machine measurements, realizes a precise and robust long-term reduction of thermal errors of machine tools. To further improve the precision and the robustness of data-based models for thermal error compensation, this publication introduces a new method to realize adaptive model inputs. This method combines the Group-LASSO (least absolute shrinkage and selection operator) for autoregressive models with exogenous inputs (ARX) and the particle swarm optimization to realize a simultaneous estimation of the optimal inputs, the model structure, and the model parameters. Additionally, the self-optimization ability of thermal error compensation models, based on the TALC, is increased by introducing error-specific action control limits to define the frequency of model updates. The newly developed methods are applied to compensate the thermal errors of a swiveling and a rotary axis of a five-axis machine tool during a long-term test series of 350 h. Randomly generated speed profiles of the linear and rotary axes as well as the spindle and changing ambient conditions ensure a high variety of thermal load cases within in the analyzed long-term test series. The results show that the prediction accuracy measured as peak-to-peak values and the robustness of the thermal error compensation models are improved by up to 36% and 58% respectively when adaptive instead of static model inputs are used. Furthermore, the compensation results of the new method outperform the previously used sequential input selection method regarding prediction accuracy and repeatability. The average peak-to-peak value of the compensated translational thermal errors is reduced by 23% and the repeatability of the corresponding compensation results is increased by 57%. Consequently, the consideration of the resulting model structure during the selection of the optimal model inputs significantly enhances the performance of the resulting data-based thermal error compensation models.

재제조를 위한 노후 선반 스핀들의 열적 안정화 설계에 관한 연구

Decrepit lathe can be functionally upgraded to like-new or with enhanced performance through remanufacturing. Remanufacturing can reduce resource consumption and waste generation. Machining accuracy is often affected by thermal errors, and spindles are one of the main sources of thermal errors in machine tools. In this study, an optimal design was developed to thermally stabilize the spindle of a machine tool to be remanufactured. The spindle bearing position, diameter of the shaft, and the length of the shaft were selected as design variables, and the object functions of the optimal design were the thermal deformation and the temperature of the spindle. Experiments were performed using computer aided design. As a result, the temperature and thermal deformation of the optimized model were decreased compared with the initial model. The optimized model can be applied to improve the accuracy of a spindle on a decrepit lathe after remanufacturing.

THE IMPACT OF KEY TEMPERATURE MEASURING POINTS ON THERMAL ERROR COMPENSATION MODEL TRANSFER BETWEEN MILLING CENTERS OF THE SAME PRODUCT LINE

Machine tool thermal errors are an important element in machined workpiece inaccuracies. In the past few decades, thermal errors have been successfully reduced by software compensation techniques such as multiple linear regression analysis, finite element method, neural network and transfer function (TF). A phenomenological approach based on TFs is used for thermal error compensation in this research. This approach respects basic heat transfer mechanisms in the MT structure and requires a minimum of additional gauges. Every MT is unique due to manufacturing inaccuracies in components, assembly processes (different preloads in bolts, deviations in the assembly and seating of components, etc.) or working environment (air-conditioned room or ordinary production hall). The aim of this paper is to investigate thermal error compensation model transfer between machines of the same product line to improve thermal error model prediction. Several milling centers of the same product line were tested with varying non-stationary activities of heat sources from linear axis movements and in varying working environments. The issue of key temperature point selection is considered.

Application of the digital model thermal errors of machine tools in automated production

The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

The temperature-sensitive point screening for spindle thermal error modeling based on IBGOA-feature selection

Thermal error compensation is a simple and efficient method to reduce thermal errors of machine tools, and the compensation effect largely depends on the temperature-sensitive points screened for the thermal error modeling. In this paper, 5 spindle heating experiments are carried out, and a new temperature-sensitive point screening method based on the Improved Binary Grasshopper Optimization Algorithm (IBGOA) feature selection is proposed. Firstly, an optimal approximation criterion is added to the Binary Grasshopper Optimization Algorithm (BGOA) for ensuring the convergence of the algorithm. And the temperature measuring points are regarded as the feature of the thermal error. Then these feature temperature point subsets are generated by IBGOA. Next, the stepwise regression analysis is carried out to remove non-significant temperature points from these subsets. And each subset is evaluated to search the temperature-sensitive points based on the cross-validation result of the multiple linear regression (MLR). Finally, for further testing the applicability of the proposed temperature-sensitive points screening method, 3 common thermal error models are established with MLR, support vector regression, and back propagation neural network respectively. Compared with the traditional fuzzy C-means clustering (FCM) temperature-sensitive points screening method, the RMSE of these models could decrease by 30–50% in the thermal drift error of X-direction, 10%–30% in the thermal tilt error of Y-direction, generally 40%–60% in the thermal elongation of Z-direction and the thermal drift error of Y-direction. The results show the superiority of the proposed IBGOA-feature selection method.

A data-driven model for thermal error prediction considering thermoelasticity with gated recurrent unit attention

It is of great significance to reduce the thermal error of machine tools. However, there is a time lag between different temperature measurement points due to the thermoelastic effect, which causes inaccurate prediction when using only the current temperature. In this paper, the GRU time series neural network with an attention mechanism is employed to establish the thermal error model of the screw, which uses historical data and sets different weights for them. In addition, since the thermal error is closely related to the working condition, the electronic control data that reflect the working condition in the CNC system are considered. Compared with several state-of-art methods, such as RNN and LSTM, the prediction results demonstrate the superiority of the proposed method. The actual machining indicates that the compensation rate exceeds 75% and can reduce the thermal error from 20 µm to 5 µm.

ENHANCEMENT OF SPECIALISED VERTICAL TURNING LATHE ACCURACY THROUGH MINIMISATION OF THERMAL ERRORS DEPENDING ON TURNING AND MILLING OPERATIONS

Achieving high workpiece accuracy is a long-term goal of machine tool designers. There are many causes of workpiece inaccuracy, with thermal errors being the most dominant. Indirect compensation (using predictive models) is a promising strategy for reducing thermal errors without increasing machine tool cost. A modelling approach using thermal transfer functions (a dynamic method with a physical basis) embodies the potential to deal with this issue. The method does not require interventions into the machine tool structure, uses a minimum of additional gauges and its modelling and calculation speed is suitable for real-time applications with fine results with up to 80% thermal error reduction. Advanced machine tool thermal error compensation models have been successfully applied on various kinds of single-purpose machines (milling, turning, floor-type, etc.) and implemented directly into their control systems. This research reflects modern trends in machine tool usage and as such is focused on the applicability of the modelling approach to describe specialised vertical turning lathe versatility. The specialised vertical turning lathe is adequately capable of carrying out turning and milling operations. Calibration of the reliable compensation model is a real challenge. The applicability of the approach during immediate switching between turning and milling operations is discussed in more detail.

CNC machine tool thermal error robust state space model based on algorithm fusion

CNC machine tool thermal error robust state space model based on algorithm fusion

Prediction of thermal error for feed system of machine tools based on random radial basis function neural network

Thermal errors affect the accuracy of computer numerical control machine tools and are produced by the thermal deformation of machine components due to temperature difference between heat source and ambient temperature of the machine tools. At present, most of the literature does not consider the randomness of the influencing factors of thermal error, leading to inaccurate predictions of machine tool thermal error. In this paper, a new inverse random model is proposed through the combination of the stochastic theory, genetic algorithm, and radial basis function neural network (RBFNN), to predict thermal error while considering the randomness of influencing factors. The randomness index of influencing factors can be identified using the inverse random RBFNN (IR-RBFNN). Furthermore, through the combination of the stochastic theory, RBFNN, and the improved exponential moving average method with abnormal data elimination, a new forward random radial basis function neural network (FR-RBFNN) is established according to the identified influencing factor random index. The models are verified through experimental results on a ball screw system. Compared with the traditional methods, the experimental data show that the proposed method provides a more accurate description of thermal errors while incorporating the randomness of factors affecting thermal error.

A Dynamic Linearization Modeling of Thermally Induced Error Based on Data-Driven Control for CNC Machine Tools

This paper proposes a novel dynamic linearization modeling method for machine tool thermal errors based on data-driven control theory, with improved accuracy and robustness under various practical working conditions of machine tool. The nonlinear, quasi-static and pseudo-hysteric characteristics of the machine tool temperature field are identified as the main causes for poor robustness in conventional thermal error mathematical models. The theoretical and practical difficulties in applying conventional modeling approaches based on the model-based control theory are demonstrated using two types of CNC machine tools as examples. The data-driven control theory is applied to dynamic linearization modeling and the developed data model has shown significant improvement over the general dynamic model in terms of model accuracy and robustness. The feasibility and effectiveness of the proposed dynamic linearization modeling method has been verified using two experiments, demonstrating excellent robustness and ability to adapt to various machining conditions and to improve machining accuracy.

Thermal Deformation Test and Modeling of Main Spindle of Numerical Control Vertical Machining Center

Thermal error is the main factor affecting the manufacturing accuracy of the numerical control vertical machining center. The thermal deformation of the machine spindle is the main source of machine tool thermal error. In view of the deficiency of single factor modeling of thermal deformation of spindle of traditional machine tools, it is measured in this paper the temperature rise and Z-axis thermal deformation of a vertical machining center under several rotating speed conditions, the test data is analyzed and processed, and the relationship curves of thermal deformation of the spindle relative to time and temperature rise under several working conditions are drawn. Binary linear regression analysis model and artificial neural network model for thermal deformation of the spindle are presented. By comparing the residual error of these two modeling methods, the error for binary regression modeling is from −7.5μm to 10μm, however, the error for neural network modeling is from −5.5μm to 8.1μm, it is shown that neural network modeling is superior to binary regression modeling.

The Thermal Error Modeling with Deep Transfer Learning

Thermal error of CNC machine tools is one of the main factors affecting the machining accuracy. The data-driven method for thermal error modeling is an effective and efficient, but they have some flaws, such as poor accuracy, bad robustness, and etc. because of having no quite enough data set and imbalanced data set. In this paper, a new method based on transfer learning for thermal error modeling is presented for solving the issue of imbalanced data set. The dataset of monitoring the temperature field of the machine tools includes monitoring data of three kinds of operating conditions, namely stopping, idling, and machining. When the fewer idling data is used to train a model, the larger stopping data are introduced as train aids. Transfer learning is adopted to fully learn the common characteristics of the two different working conditions, which can effectively solve the problem of imbalanced dataset. The experimental results prove that our method have better performance than other methods trained only with limited idling data.

Thermal Error Minimization of a Turning-Milling Center with Respect to its Multi-Functionality

Achieving high workpiece accuracy is a long-term goal of machine tool designers. Many causes can explain workpiece inaccuracy, with thermal errors being the most dominant. Indirect compensation (using predictive models) is a promising thermal error reduction strategy that does not increase machine tool costs. A modeling approach using transfer functions (i.e., a dynamic method with a physical basis) has the potential to deal with this issue. The method does not require any intervention into the machine tool structure, uses a minimum of additional gauges, and its modeling and calculation speed are suitable for real-time applications that result in as much as 80% thermal error reduction. Compensation models for machine tool thermal errors using transfer functions have been successfully applied to various kinds of single-purpose machines (milling, turning, floor-type, etc.) and have been implemented directly into their control systems. The aim of this research is to describe modern trends in machine tool usage and focuses on the applicability of the modeling approach to describe the multi-functionality of a turning-milling center. A turning-milling center is capable of adequately handling turning, milling, and boring operations. Calibrating a reliable compensation model is a real challenge. Options for reducing modeling and calibration time, an approach to include machine tool multi-functionality in the model structure, model transferability between different machines of the same type, and model verification out of the calibration range are discussed in greater detail.

Thermal error and energy-saving performance evaluation of the energy-saving machine tool using new structural materials

Thermal error and energy-saving performance evaluation of the energy-saving machine tool using new structural materials

Dynamic temperature gradient and unfalsified control approach for machine tool thermal error compensation

In this work, a novel machine tool thermal error modeling method based on dynamic temperature gradient is proposed, and a thermal error compensation method based on unfalsified control is developed. The dynamic temperature gradient is used to optimize the locations of temperature measuring points on the machine tool. Real-time compensation for the thermal error can be achieved using the developed compensation method by establishing the correlations between dynamic temperature gradient and thermal error in the machine tool. Different from traditional model-based methods, the developed compensation approach does not rely on an accurate model of the thermal error but instead uses online input/output data to adaptively select the best controller at any moment, thereby improving thermal error prediction accuracy and robustness. The effectiveness of the developed thermal error compensation method is demonstrated on a turning center, where the spindle thermal error is compensated during the manufacturing of 120 inner bore parts and 120 shaft parts. After compensation using the proposed approach, thermal errors are reduced from 27 ώm to 9 ώm for the inner bore parts and from 31 ώm to 11 ώm for the shaft parts, respectively.

Thermal error compensation in CNC machine tools using measurement technologies

The required machining accuracy can be realized by compensating the thermal error of the machine tool. The paper presents a method of constructing a system of compensation for the thermal error of the machine tool. The method is based on the part measurement system installed on the machine. A review of studies in the field of precision machining was conducted. The review showed that the methods of correction of machining errors caused by dynamic and static factors using OMM-technology (On Machine Measurement) is presented in the literature. At the same time, the methods of compensation of the thermal error of machine tool built on the use of OMM-technology are poorly represented. Therefore, the practical implementation of control algorithms of the working bodies of the machine allowing one to compensate for its thermal error using OMM-technology is in demand. The methodology contains seven main stages, methodologically covering four areas of research. In addition to the experimental, mathematical and measurement areas of research, they also distinguish the area of software development (preparation of NC programs for CNC machines). It is proved that the presented methodology allows one to develop own automated system of compensation of a thermal error for any machine tool. It is shown that the most important directions of the framework for the improvement of the effectiveness of such system are to ensure the completeness of the experimental base and the accuracy of the adjustment of the thermal error compensation algorithms of the machine tool.

The average effect of multi-divisions cutting method on thermal error in cutting horizontal grooves array on roller mold

Microstrutured surfaces can be manufactured by roll-to-roll process with roller mold in a high precision and high efficient way. The roller mold machined by drum roll lathe has a large cutting area and takes long time, and thus, the machining precision of roller mold is very sensitive to the thermal error of machine tool. This study analyzes the influence of thermal error on the machining horizontal grooves array on roller mold and proposed a processing method to improve the machining precision. First, the thermal error of hydrostatic guideways system in drum roll lathe was analyzed by simulation and measured through experiments, respectively. Given the diamond tool position offset caused by thermal error, the pitch error of horizontal grooves array with sequential cutting method was discussed. And then, multi-divisions cutting method is proposed to average the pitch error to multiple horizontal grooves instead of cumulative error in the seam position of horizontal grooves array, compared with sequential cutting method. Finally, the experiments with Four Divisions Method were carried out to verify the validity of multi-divisions cutting method. The experimental results demonstrate that multi-divisions cutting method can considerably improve the machining precision of horizontal grooves array.

Research on High-Precision Form Grinding Technology of Gear Based on Ambient Temperature Adaptability

The thermal error of high-precision computer numerical control (CNC) form grinding machine has become the critical factor affecting its locating precision. Because the thermal error is more complicated to be measured directly, most of the measurement tests are usually aimed at no-load conditions. In this article, a method to evaluate the machine tool thermal error based on the principle of tooth profile error through the gear grinding precision is presented. Based on high-precision CNC form grinding machine need to maintain isoperibol, this paper proposed the ambient temperature as a variable, for different ambient temperature to measure the thermal error of the machine tool. According to the measurement results of the thermal error, the method of partial least squares neural network is used to structure the thermal error prediction model of the machine tool. The experimental data show that the grade of the gear precision can reach four levels at different temperatures. This approach shows a promising prospect in the application of high-precision CNC gear form grinding machine in the future.

Sensor Selection Method to Accurately Model the Thermal Error in a Spindle Motor

Thermal error in machine tools arises from error in positioning the tool due to thermal distortion of the machine structure. Thermal error can be effectively reduced by implementing a compensation algorithm into the machine control loop. The compensation algorithm is derived from a disturbance model or mathematical representation of the thermal error. In this paper, thermal error in a spindle motor assembly is predicted from temperature measurement through the disturbance model. Model reduction technique is applied to eliminate the temperature sensor, which has least effect on the model output (thermal error). Applying the sensor selection method resulted in a reduced number of temperature sensors (10 to 4) required to achieve the same model prediction accuracy. In the final experiment, the spindle motor is subjected to a 5-h cyclical heat load and the maximum prediction accuracy achieved using the reduced set sensors is 1.5 $\mu$ m (time averaged accuracy of 0.77 $\mu$ m).

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.