Thermal error forecast and performance evaluation for an air-cooling ball screw system

Xu, Zhe-Zhu; Liu, X.J.; Kim, H.K.; Shin, Jung H.; Lyu, Sung-Ki

doi:10.1016/j.ijmachtools.2011.04.001

Cited by 120 publications

(55 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The search continues for an effective, universal, simple and cheap method of compensating for the elongation of ball screw transmission systems [6][7][8][9][10][11][12][15][16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

2017

View full text Add to dashboard Cite

Original scientific paper The need to improve the accuracy of positioning of a servo-drive was the stimulus for research on a new sensorless method for compensation of thermal deformations of ball screws, enabling predictive compensation of the elongation of such a screw based on historical data. Models have been developed for the predictive compensation of thermal deformations of ball screws in CNC machines, in the form of single-directional multi-layered neural networks with error back-propagation (MLP), radial basis function neural networks (RBF) and Kohonen networks. Neural networks were developed with different structures and learning parameters, and these networks were compared. Models were evaluated in terms of the effectiveness of operation of the networks. The models were tested on real data. Keywords: ball screw; CNC machine tool; neural network; prediction model; thermal compensation Predvidiva kompenzacija toplinskih deformiranja kuglastih vijaka u CNC strojevima primjenom neuronskih mrežaIzvorni znanstveni članak Potreba za povećanjem točnosti pozicioniranja servo-pogona bila je poticaj za istraživanje nove metode bez senzora za kompenzaciju toplinskih deformiranja kuglastih vijaka, koja će omogućiti predvidivu kompenzaciju izduženja takvih vijaka na temelju prikupljenih podataka. Razvijeni su modeli za predvidivu kompenzaciju toplinskih deformiranja kuglastih vijaka u CNC strojevima, u obliku jednosmjernih višeslojnih neuronskih mreža s unatražnim rasprostiranjem greške (MLP), neuronskih mreža s funkcijom radijalne baze (RBF) i Kohonen mreža. Razvijene su neuronske mreže s različitim strukturama i parametrima učenja, i te su se mreže uspoređivale. Modeli su se procjenjivali prema učinkovitosti mreža. Modeli su se ispitivali s realnim podacima.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

2017

View full text Add to dashboard Cite

show abstract

“…가공 공작기계에 대한 열변형 연구가 많이 수행되어 왔으며 [1][2][3] , 또한 이송축 열변형이 중요함에 따라서, 그와 관련된 연구도 수행되어져 왔다. 유한 차분법 또는 유한 요소법을 활용하여 열전달 및 열변형 해석을 수행하였다 [4][5][6][7] . 볼 스크루의 각 위치에 일정 시간 동안 균 일한 열유속을 적용하고, 다시 일정 시간 동안 냉각하였다.…”

Section: 서 론 열 진동 절삭 하중 및 제어 등은 공작기계의 가공 정밀도에 많 은 영향을 미치고 있으며 그 중에unclassified

Evaluation of Thermal Characteristics for a Feeding Axis of Machine Tools Using Finite Element Analysis

Lee¹,

Choi²

2013

Journal of manufacturing engineering & technology

View full text Add to dashboard Cite

Article history:In this study, two finite element (FE) models were developed to evaluate the thermal characteristics of a feeding axis of a CNC lathe. One was used for analysis of heat transfer to identify the temperature distribution of the feeding axis and then, the other was used for analysis of thermal deformation to evaluate its structural behavior based on the temperature distribution. The FE models were based on the test standard for the axial thermal displacement. The feeding velocity was composed of three steps: the ascending, constant, and descending velocities. Therefore, the heat generation and convection coefficient were calculated for each velocity and applied to the thermal FE model. The convection coefficient for the ball screw rotation was based on an experimental equation. The result of the analytical thermal displacement was compared with that of the experimental displacement to verify the finite element models.

show abstract

“…The difficulty in the lumped-mass method is how to evaluate the parameters, such as the convection coefficient and lumped-mass. Several researchers [9,10] used the modified lumped-mass method to forecast the real-time temperatures of a ball screw system. Influence coefficients are used to compensate the deviations in parameter evaluation.…”

Section: Introductionmentioning

confidence: 99%

Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

Wang¹,

Li²,

Cai³

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper presents a modeling methodology for the thermal error of machine tool. The temperatures predicted by modified lumped-mass method and the temperatures measured by sensors are fused by the data fusion method of Kalman filter. The fused temperatures, instead of the measured temperatures used in traditional methods, are applied to predict the thermal error. The genetic algorithm is implemented to optimize the parameters in modified lumped-mass method and the covariances in Kalman filter. The simulations indicate that the proposed method performs much better compared with the traditional method of MRA, in terms of prediction accuracy and robustness under a variety of operating conditions. A compensation system is developed based on the controlling system of Siemens 840D. Validated by the compensation experiment, the thermal error after compensation has been reduced dramatically.

show abstract

Thermal error forecast and performance evaluation for an air-cooling ball screw system

Cited by 120 publications

References 12 publications

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Evaluation of Thermal Characteristics for a Feeding Axis of Machine Tools Using Finite Element Analysis

Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

Contact Info

Product

Resources

About