Analysis and Prediction of Thermally Induced Positioning Error in Hydrostatic Lead-Screw Feed Systems
Hydrostatic lead screw is a key transmission component of ultra-precision machine tools, yet the induced thermal errors present a major hurdle to maintaining positioning accuracy. Existing thermal analysis methods are often limited by the empirical placement of temperature sensors, leading to an inc...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025022959 |
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| Summary: | Hydrostatic lead screw is a key transmission component of ultra-precision machine tools, yet the induced thermal errors present a major hurdle to maintaining positioning accuracy. Existing thermal analysis methods are often limited by the empirical placement of temperature sensors, leading to an incomplete understanding of the complex temperature field. To address this gap, this research proposes a novel method that integrates simulation with a data-driven optimization strategy. The core of our approach involves using the K-means clustering algorithm to optimize the selection of temperature measurement points on the hydrostatic screw nut, ensuring a more representative thermal characterization. Simulations and experimental verification were conducted to assess the temperature rise characteristics of the helical oil film using this optimized configuration. Results indicate that rotational speed has a minimal effect on the temperature rise, while the thermal positioning error escalates with the temperature of these critical points. This research provides a robust methodology for thermal analysis, supporting the wider application of hydrostatic lead screws in ultra-precision machine tools. |
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| ISSN: | 2590-1230 |