A prediction and evaluation system of the impact factors on the performance of the aerostatic slider. (April 2018)

• Record Type: Journal Article
• Authors: Chen, Dongju; Cui, Xianxian; Fan, Jinwei
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 50(2018)
• Page Start: 213
• Page End: 221
• Publication Date: 2018-04
• Subjects: Aerostatic slider -- Impact factor -- Cross correlation -- Neural network -- Predicting; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2017.09.016
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: An evaluated method for the influence factor of performance of slider is proposed. The relationship between the stiffness and diameter and film thickness is deduced. Predicating model of the slide with neural network is trained. Abstract: In order to evaluate the effect of the diameter of the restrict orifice and the gas film gap on the performance of the aerostatic slider, cross-correlation analysis method and neural networks should be used to predicate and evaluate the influence degree and the impact factors. The load capacity and stiffness of the airflow film with different diameters and gas gaps are calculated with the modified Reynolds equation, and the prediction and approximation value are showed by the BP neural network. The correlation degree between the stiffness, which predicated by the neural network and the experimental results of the stiffness is obtained with the cross-correlation analysis method. The correlation coefficient of the stiffness of the gas film thickness and measured result is close to 1, which indicates that the gas film thickness of the slider is the main errors in test results. It provides the evaluation method to identify the effect of the influence factors on the performance of the aerostatic slider.