Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
Source abstract
Abstract
In this paper, we propose an artificial neural network (ANN) predictive model to identify the linear guide preload based on the measured vibration features of the feeding stage. In this study, the relationship between the contact stiffness and preload level of a linear guide was investigated by an experimental analysis. Furthermore, the stage was assembled with different linear guide preloads for the motion test to assess the vibrations. Vibration levels with changes in preload values and feeding rates were examined. The predictive models were established and verified based on a dataset collected from tests using an ANN approach. The ANN models were shown to have an excellent accuracy of 96.5% in the training datasets, which were collected from stages with sliding blocks rated at consistent preloads. The average percentage prediction error in the verification dataset was approximately 8.54–11.23%. This is probably because the stage with an unevenly distributed preload in the sliding blocks induces vibration with more fluctuation, which eventually affects the prediction accuracy. The results verify the feasibility of online preload identification for the condition monitoring of the feeding system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydraulic and Pneumatic Systems · Gear and Bearing Dynamics Analysis · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Taichung 41130, Taiwan
Hiwin Technologies Corp., Taichung 41130, Taiwan
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