Compensation for the thermal error of a multi-axis machining center

Wang, Yiding; Zhang, Guoxiong; Moon, Kiwon; Sutherland, John W.

doi:10.1016/s0924-0136(97)00291-4

Cited by 66 publications

(42 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Srivastava et al established error model of five axis machining center by HTM [32]. Wang et al proposed offline and online thermal error compensation model based on the grey system theory [33]. South Korean scholar Lee et al established thermal error model of horizontal machining center based on fuzzy logic strategy [34].…”

Section: Research Progress Of Thermal Deformation Control and High Efmentioning

confidence: 99%

The Research Status and Progress of Heavy/Large Hydrostatic Thrust Bearing

Wang

et al. 2014

Advances in Mechanical Engineering

View full text Add to dashboard Cite

How to improve the rotation speed of heavy/large CNC vertical lathe, the machining efficiency, and machining precision is one of the key issues which need to be solved urgently. Hydrostatic thrust bearing is the key part to the heavy/large CNC vertical lathe; its performance directly affects the machining quality and operation efficiency. This paper analyses the latest research results from the perspective of the mechanical properties of hydrostatic thrust bearing, oil film lubrication, static pressure bearing thermal deformation, and the high efficiency refrigeration and evaluates the future scientific research direction in this area. Analysis shows that with the development of hydrostatic thrust bearing to the high speed, high precision, high efficiency, high stability, high multifunction, and high power, the study of hydrostatic thrust bearing will focus on the optimal design of the oil chamber to produce the least amount of heat, how to control the thermal deformation of hydrostatic thrust bearing, and the high efficiency refrigeration to ensure the machining accuracy of CNC equipment.

show abstract

Section: Research Progress Of Thermal Deformation Control and High Efmentioning

confidence: 99%

The Research Status and Progress of Heavy/Large Hydrostatic Thrust Bearing

Wang

et al. 2014

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…It does not rely on statistical method to deal with the grey quantity. It deals directly with the original data, and searches the intrinsic regularity of data (12) . The grey system theory include the following fields: (a) grey generating, (b) grey relational analysis, (c) grey forecasting, (d) grey decision making, and (e) grey control.…”

Section: Gm(1 N) Modelmentioning

confidence: 99%

“…The second area is empirical modeling based on the measurement of thermal deformation and temperature at discrete representative points on the machine tools. Examples include engineering judgment (3) , regression analysis (5) , neural network (6) - (8) , grey system theory (12) , and fuzzy logic (15) . Empirical models have been demonstrated to show satisfactory prediction accuracy in many applications even though each method has its own shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Thermal Error Modeling of a Machining Center Using Grey System Theory and Adaptive Network-Based Fuzzy Inference System

Wang

2006

JSME Int. J., Ser. C

View full text Add to dashboard Cite

Thermal effect on machine tools is a well-recognized problem in an environment of increasing demand for product quality. The performance of a thermal error compensation system typically depends on the accuracy and robustness of the thermal error model. This work presents a novel thermal error model utilizing two mathematic schemes: the grey system theory and the adaptive network-based fuzzy inference system (ANFIS). First, the measured temperature and deformation results are analyzed via the grey system theory to obtain the influence ranking of temperature ascent on thermal drift of spindle. Then, using the highly ranked temperature ascents as inputs for the ANFIS and training these data by the hybrid learning rule, a thermal compensation model is constructed. The grey system theory effectively reduces the number of temperature sensors needed on a machine structure for prediction, and the ANFIS has the advantages of good accuracy and robustness. For testing the performance of proposed ANFIS model, a real-cutting operation test was conducted. Comparison results demonstrate that the modeling schemes of the ANFIS coupled with the grey system theory has good predictive ability.

show abstract

“…At present, grey system theory has been applied to predict and analyze the practical problems in different fields. Wang et al [14] presented a systematic methodology for the thermal-error correction of a machine tool and used the grey system model to predict and compensate for the thermal error. Li et al [15] studied the optimization of the thermal sensors' placement on machine tools based on grey system theory and reduced the temperature variables in the thermal error model significantly.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

et al. 2017

View full text Add to dashboard Cite

Abstract. In the process of the traditional fixture design for sheet metal part based on the "N-2-1" locating principle, the number of fixture locating points is determined by trial and error or the experience of the designer. To that end, a new design method based on grey theory is proposed to determine the number of sheet metal fixture locating points in this paper. Firstly, the training sample set is generated by Latin hypercube sampling (LHS) and finite element analysis (FEA). Secondly, the GM(1, 1) grey model is constructed based on the established training sample set to approximate the mapping relationship between the number of fixture locating points and the concerned sheet metal maximum deformation. Thirdly, the final number of fixture locating points for sheet metal can be inversely calculated under the allowable maximum deformation. Finally, a sheet metal case is conducted and the results indicate that the proposed approach is effective and efficient in determining the number of fixture locating points for sheet metal.

show abstract

Compensation for the thermal error of a multi-axis machining center

Cited by 66 publications

References 20 publications

The Research Status and Progress of Heavy/Large Hydrostatic Thrust Bearing

The Research Status and Progress of Heavy/Large Hydrostatic Thrust Bearing

Thermal Error Modeling of a Machining Center Using Grey System Theory and Adaptive Network-Based Fuzzy Inference System

Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

Contact Info

Product

Resources

About