Experimental analysis of the wear coefficient for the rolling linear guide

Wang, Xiaoyi; Zhou, Chang-Guang; Ou, Yi

doi:10.1177/1687814018821744

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…The wear amount was experimentally found to increase with an increase in the running distance, which further reduced the contact deformation and stiffness of the linear guides [8,9]. Further, the preload on the rolling element was evaluated as a decreasing trend from the measured friction during the run-in process [10]. Recently, Zhou et al [11] illustrated the preload degradation behavior of the roller guide using an analytical approach and verified it experimentally.…”

Section: Introductionmentioning

confidence: 98%

Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 98%

Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

et al. 2021

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“…While friction and preload do not directly cause wear, they can accelerate the wear rate and alter the wear mechanism, thereby reducing the performance and service life of RLG specifically, and the overall system performance in general. Additionally, there is a study focusing on the factors that directly affect the wear of RLG by showing that a new theoretical and experimental method for the wear coefficient of rolling linear guides according to Archard's wear theory [16]. The direct and indirect influencing factors are crucial in understanding the wear process of roller guides to develop optimal design and reliability in using RLG and other rolling components in CNC machine tools.…”

Section: Introductionmentioning

confidence: 99%

A Method for Predicting Wear Behaviours of Roller Linear Guides Under the Influence of the Relative Humidity and Ambient Temperature

Pham

2024

Tribol. Ind.

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The wear of roller linear guides (RLG) has a direct impact on the tooling precision of CNC machine tool. Wear depends on working conditions: load P, speed V, work environment which includes relative humidity RH and ambient temperature T. In this paper the mathematical regression function describes the relationship between the wear amount U of RLG with the three factors P, RH and T built on the basis of experimental research. The results show that the wear regression function U has an exponential form, which is consistent with experiments about the influence of climatic environment. It describes the simultaneous impact of three factors P, RH, T that affect wear of CNC machine tool rolling guide. With Vietnam's hot and humid environmental conditions, relative humidity RH% = 51 - 99%, temperature T = 15 - 450C, and load P = 2 - 6kgf, the influence on rolling guide wear of normal load P is 2 times the humidity RH% and about 3.8 times that of temperature T. Therefore, it is possible to predict the change in wear amount according to two environmental factors when loading P at certain values.

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Adaptive Thermal Model for Structure Model Based Correction

Thiem

Rudolph

Krahn

et al. 2023

Lecture Notes in Production Engineering

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This paper discusses structure model based correction of thermal induced errors at machine tools. Using a machine model evaluated in thermal real-time, the thermal induced errors at the tool center point (TCP) are calculated based on information gotten from the machine control (e.g., axes velocities, positions, and motor currents) and ambient temperature. The machine model describes the physical relationships and considers the structure and structural variability resulting in traverse movements of the feed axes – the so-called structure model. To create this, finite elements are used as thermal and thermo-elastic models, and model order reduction (MOR) techniques are used to enable the calculation of high-resolution models in thermal real-time. Subsequent parameter updates can improve the accuracy of the initial parameter set of thermal models. A systematic procedure developed for this purpose and its application to a demonstrator machine are presented. For the update, parameters are selected which can change over the operating time, e.g., due to wear. Temperature sensor positions are chosen, sensitive to changes in these parameters. Simulations with parameters varied in a plausible range are used to determine whether parameter optimizations are reasonable. The parameter optimization runs in a trusted execution environment (TEE) on a server in parallel to the calculation of the correction model on the machine control. The confidential input data of the model and the model itself have to be protected from unauthorized access. The efficient model calculation and parameter optimization in a secure server environment leads to an adaptive thermal model (digital twin).

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Experimental analysis of the wear coefficient for the rolling linear guide

Cited by 9 publications

References 29 publications

Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

A Method for Predicting Wear Behaviours of Roller Linear Guides Under the Influence of the Relative Humidity and Ambient Temperature

Adaptive Thermal Model for Structure Model Based Correction

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