Machine Tool Thermal Error Reduction—an Appraisal

1999 ◽  
Vol 213 (1) ◽  
pp. 1-9 ◽  
Author(s):  
S R Postlethwaite ◽  
J P Allen ◽  
D G Ford
2020 ◽  
Vol 14 (3) ◽  
pp. 475-483
Author(s):  
Martin Mareš ◽  
◽  
Otakar Horejš ◽  
Jan Hornych

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.


2010 ◽  
Vol 455 ◽  
pp. 621-624
Author(s):  
X. Li ◽  
Y.Y. Yu

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.


2018 ◽  
Vol 10 (6) ◽  
pp. 168781401877862
Author(s):  
Yanfang Dong ◽  
Zude Zhou ◽  
Lihai Chen

As a key component of the machine tool spindle, bearing has critical influences on the spindle thermal error. In particular, the installation errors of bearing have considerable effects upon the spindle thermal error by altering the bearings’ internal contact angles, contact loads, and friction torques for different ball positions, but have yet to be fully elucidated. In this article, the influence of installation errors on the resulting spindle thermal error was evaluated using both empirical methods and simulation method, with the ultimate aim of reducing installation error. Deviations within the bearing support were used to simulate bearing parallel misalignment; bearing parallel misalignment running model was built, and an analysis and comparison of various conditions were used to determine the influence, showing that the parallel misalignment has significant influence on the spindle Z direction thermal error.


2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.


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