Abstract
In modern solutions of machine tools and technological devices for precision machining, rolling guides are used. Their advantages are low resistance to movement and the possibility of obtaining very good repeatability of positioning of working units. The main structural element is ball screws. During their utilization, there is gradual wear of the cooperating elements of the ball screw-nut assembly and deterioration of their functional properties. As the work is performed by the screw, its working surfaces are subject to abrasive wear, which results in a gradual loss of the accuracy of the machine and the technological quality of the manufactured products. It can also lead to failure due to damage to the ball screws. Applied preventive actions come down mainly to periodic inspections and maintenance of mechanisms. In order to prevent excessive wear of the rolling guides or even machine failure, the authors of the article proposed a prognostic model to determine the wear intensity of the ball screw based on backlash measurements in the lead screw-nut assembly. The article presents a developed model for the prediction of the wear intensity of the rolling guides based on artificial neural networks. The authors obtained a model of prediction accuracy of 81%.
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Krajewska-Śpiewak, J., Serwatka, Ł., Gawlik, J. (2024). Prediction of the Wear Intensity of Rolling Guides with the Use of a Neural Network. In: Burduk, A., Batako, A.D.L., Machado, J., Wyczółkowski, R., Dostatni, E., Rojek, I. (eds) Intelligent Systems in Production Engineering and Maintenance III. ISPEM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-44282-7_7
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DOI: https://doi.org/10.1007/978-3-031-44282-7_7
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