Dimensional, geometrical, thermal and tool deflection errors compensation in 5-Axis CNC milling operations

Virtual manufacturing systems can provide useful means for products to be manufactured without the need of physical testing on the shop floor. As a result, the time and cost of part production can be decreased. There are different error sources in machine tools such as tool deflection, geometrical deviations of moving axis and thermal distortions of machine tool structures. Some of these errors can be decreased by controlling the machining process and environmental parameters. However other errors like tool deflection and geometrical errors which have a big portion of the total error, need more attention. This paper presents a virtual machining system in order to enforce dimensional, geometrical and tool deflection errors in three-axis milling operations. The system receives 21 dimensional and geometrical errors of a machine tool and machining codes of a specific part as input. The output of the system is the modified codes which will produce actual machined part in the virtual environment.

The 5-axis CNC machine tools are used for manufacturing free form surfaces of sophisticated parts such as turbine blades, airfoils, impellers, and aircraft components. The virtual machining systems can be used in order to analyze and modify the 5-axis CNC machine tools operations. Cutting forces and cutting temperatures induce deflection errors in thin-walled structures such as impeller blades through machining operations. Thin-walled impeller blades' flexibility can result in machining errors such as overcutting or undercutting. So, decreasing the deflection error during machining operations of impeller blades can achieve the desired accuracy in produced parts. Optimized machining parameters can be obtained to minimize the deflection of machined impeller blades. In terms of precision and efficiency enhancement in component production processes, a virtual machining system is developed to predict and minimize deflection errors of 5-axis milling operations of impeller blades. The deflection error in machined impeller blades is calculated by using finite element analysis. The optimization methodology based on the genetic algorithm is applied to minimize the deflection error of impeller blades in machining operations. To validate the integrated virtual machining system in the study, the impeller is milled by using a 5-axis CNC machine tool. The CMM machine is used in order to measure and analyze deflection error in the machined impeller blades. As a result, by using the developed virtual machining system in the study, accuracy and efficiency in 5-axis milling operations of impellers can be increased.

In the process of thermal error modeling of machine tools, the error information is not transparent and the data characteristics are not comprehensive. The Dijkstra algorithm provides an optimization for the classical algorithm, and improving the accuracy of machine tools is an important issue in advanced manufacturing technologies. In this paper we study two methods of error avoidance and error compensation. For CNC machine tools, computer real-time error compensation technology is an economical and effective basic approach. The application of the folded Dijkstra algorithm to realize the compensation technology of the full error model of geometrical machine tool including geometric error and thermal error is put forward. The practical results show the geometric error and thermal error are compensated on 3-axis and 4-axis CNC machine tools, and the accuracy of the error model and the practicality of the compensation method.

Thermal error robust modeling method for CNC machine tools based on a split unbiased estimation algorithm

An approach to identify and select optimal temperature-sensitive measuring points for thermal error compensation modeling in CNC machines: A case study using cantilever beam

Virtual manufacturing systems carry out the simulation of manufacturing processes in digital environment in order to increase accuracy as well as productivity in part production. There are different error sources in machine tools, such as tool deflection, geometrical deviations of moving axis, and thermal distortions of machine tool structures. The errors due to tool deflection are caused by cutting forces and have direct effects on dimensional accuracy, surface roughness of the parts, and efficient life of the cutting tool, holder, and spindle. This paper presents an application of virtual machining systems in order to improve the accuracy and productivity of part manufacturing by monitoring and minimizing the tool deflection error. The tool deflection error along machining paths is monitored to present a useful methodology in controlling the produced parts with regard to desired tolerances. Suitable tool and spindle can also be selected due to the ability of error monitoring. In order to minimize the error, optimization technique based on genetic algorithms is used to determine optimized machining parameters. Free-form profile of virtual and real machined parts with tool deflection error is compared in order to validate reliability as well as accuracy of the software.

The purpose of this study was to improve the accuracy of three-axis CNC Milling Vertical engines with a general approach by using mathematical modeling methods of machine tool geometric errors. The inaccuracy of CNC machines can be caused by geometric errors that are an important factor during the manufacturing process and during the assembly phase, and are factors for being able to build machines with high-accuracy. To improve the accuracy of the three-axis vertical milling machine, by knowing geometric errors and identifying the error position parameters in the machine tool by arranging the mathematical modeling. The geometric error in the machine tool consists of twenty-one error parameters consisting of nine linear error parameters, nine angle error parameters and three perpendicular error parameters. The mathematical modeling approach of geometric error with the calculated alignment error and angle error in the supporting components of the machine motion is linear guide way and linear motion. The purpose of using this mathematical modeling approach is the identification of geometric errors that can be helpful as reference during the design, assembly and maintenance stages to improve the accuracy of CNC machines. Mathematically modeling geometric errors in CNC machine tools can illustrate the relationship between alignment error, position and angle on a linear guide way of three-axis vertical milling machines.

With rapid development in the technologies of high precision machining and the ever increasing demand for high accuracy in the automation industry, addressing accuracy problems due to geometric and thermal errors are becoming more critical in recent years, especially thermal errors which may contribute up to 75% of the overall errors in the system. Retrofitting the mechanical design or maintaining the operational temperature may not be feasible and can significantly increase cost. An error compensation method is more efficient and cost effective. In this paper, a displacement and thermal error compensation approach is proposed and developed based on radial basis functions. Feedback control is designed in both position control subsystem and temperature control subsystem. Raw position data is measured using the laser interferometer and the raw temperature data is measured using a thermistor. The overall geometric errors are related to both movement positions and the machine operating temperatures, so a 2-D RBF network is designed and trained to model and estimate the geometric errors. The RBFs are then used to compensate the error. The experimental results showed that the proposed approach can help improve the system performance and accuracy effectively.

With the structure design and manufacture of CNC machine tool becoming more and more perfect, its geometry errors have been controlled, thermal error generated by thermal deformation of CNC machine tool is the main error, which has strongly influence on the CNC machine tool's process precision. Thermal error compensation is an important method for increasing the process precision of CNC machine tool, and the neural networks technology is the major tool in thermal error modeling and compensation. In this paper, some applications of neural networks in thermal error modeling and compensation are introduced, and they are demonstrated by examples, the results prove that researches on artificial neural networks and relative algorithms have good effects on CNC machine tool's thermal error modeling and compensation.

In CNC machine tools, transient temperature variation in the headstock assembly is the major contributors for spindle thermal error. The compensation of thermal error is critical for ensuring the accuracy of machine tool. The performance of an error compensation system depends largely on the accuracy and robustness of the thermal error model. In the present work, a robust thermal error model is developed for minimizing the error in lateral direction of the spindle which significantly influences the geometrical accuracy of the workpiece. Analysis-of-variance (ANOVA) is applied to the results of the experiments in determining the percentage contribution of each individual temperature key point against a stated level of confidence. Based on the analysis of existing approaches for thermal error modeling of machine tools, an approach of LASSO (least absolute shrinkage and selection operator) is proposed in order to avoid the multi collinearity problem. The proposed method is an innovative variable selection method to remove redundant or unimportant temperature key points in the linear thermal error model and minimize the residual sum of squares. The predictive error model is found to have better robustness and accuracy in comparison to the combination of grey correlation and step wise linear regression for error compensation of CNC lathe. Keywords: Analysis Of Variance (ANOVA), CNC Machine Tool, Grey Correlation Analysis (GCA), Headstock Assembly, LASSO Regression, Mean Absolute Deviation (MAD), Mean Square Error (MSE), Robustness, Standard Deviation (SD), Thermal Error.

In order to improve the machine accuracy,a synthesis modelling method of geometric and thermal error is presented.Machine error distribution law is obtained through the analysis of machine error data at varying temperatures.Based on the different characteristics of geometric error and thermal error,error separation is carried out.Synthesis mathematical model for geometric and thermal error is proposed by using polynomial fitting and linear fitting.Online real-time error compensation is implemented by applying the computer numerical control(CNC) external machine coordinate system shift function and a novel developed synthesis error real-time compensation system.This error compensation method concerns the geometric error and its variations at different temperatures,thus a comprehensive analysis is made on the error and its regularity in the overall temperature rise to the heat steady-state.The inspection certificates that the maximum positioning error in normal temperature is reduced from 44.1 μm to 3.6 μm by applying the error compensation method and real-time compensation system,which is compensated 91.8% compared with no compensation.After the temperature rise,the maximum positioning error in normal temperature is reduced from 26.0 μm to 5.1 μm,which is compensated 80.4% compared with no compensation.The machine accuracy is effectively improved.

The Reynolds number of turbine blades can be reduced by decreasing the surface roughness of the blades, which decreases energy loss in energy production systems. Due to the challenges of the polishing process in reducing roughness of machined blades, the application of optimized machining parameters to decrease surface roughness is a practical solution. Virtual machining system is explored in this research to improve the efficiency of machined parts. The aim of this research is to provide a virtual machining system that can predict and effectively reduce roughness in gas turbine blade 5-axis machining operations. The machining parameters were optimized using Genetic algorithm to reduce surface roughness. The turbine blades were machined using a 5-axis CNC machine tool, and the surface roughness of the machined parts was measured using a CMM machine to validate the developed methodology in the study. The optimized machining parameters result in a 26.2% reduction in observed surface roughness and a 27.3% reduction in expected surface roughness in the machined blades. The developed virtual machining system can be used to improve the surface quality of machined turbine blades and hence improve the efficiency of gas turbines during the power production in gas turbines.

The article is devoted to increasing the volumetric accuracy of multi-axis machine tools. Based on the model of measurement and calculation of the volumetric geometric error of machine tools presented by the authors, a method has been developed which allows to signifi cantly improve the accuracy of the movement of the operating unit of a three-axis metal-cutting machine tool to a given point. The astatic law and the calculation of the coordinate correction according to the differential geometric model of the volumetric error are used to control the movement of the operating unit. The developed method is implemented programmatically and allows to perform the correction in the control program represented in G-code. As a result of experiments on software correction of volumetric errors according to the developed method, conducted at the State Engineering Centre of the Moscow State University of Technology “STANKIN” on a threeaxis CNC machine tool, a signifi cant reduction (up to 90 %) of geometric volumetric errors was shown. The developed method can be used to improve the accuracy of metal-cutting machine tools by creating postprocessors that calculate corrections to the coordinates set in the control system based on the results of interferometric measurements.Keywords: volumetric accuracy, interference measurements, three-axis machine tools, machine tool accuracy, geometric errors, astatic control law, mathematical modelling, differential geometry

The characteristics of geometric errors in CNC machine tools have been investigated in detail. These characteristics include position dependence, relativity, synthesis, and continuity. Tests have been designed to verify these properties, which can be used to guide the error measurement procedure. A new measurement procedure, called the relay method, has been developed. This method has high resolution and can be applied to measure geometric errors in the whole workspace. A machine has been calibrated by the relay method, and the results of error compensation show that the machine tool accuracy has been improved.

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.