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The International Journal of Advanced Manufacturing Technology

Erschienen in:

03.11.2021 | ORIGINAL ARTICLE

RTCP test axis motion planning for five-axis machine tool dynamic performance using observability optimization based on modified genetic algorithm

verfasst von: Qicheng Ding, Wei Wang, Jiexiong Ding, Jing Zhang, Chong Hu, Fengmin Lei, Li Du, Liping Wang

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 1-2/2022

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Abstract

In the five-axis CNC machining process, the dynamic tracking error due to servo dynamic performance deficiency is a main cause of processing inaccuracy during precise high-speed machining. The rotation tool center point (RTCP) test is commonly used to measure the dynamic performance of five-axis machine tools. The key to the capability of the RTCP test is axis motion planning in the test process. However, the axis motion plans for RTCP tests are usually based on simple motion instruction or engineering experience; the mechanism of the discrepancy between different axis motion plans is unclear. In this study, the axis motion planning process for RTCP dynamic performance tests is analyzed, and a novel axis motion planning method is proposed. The axis motion planning process is directly connected to the mechanism of dynamic tracking error; error observability is used as the index to guide RTCP axis motion planning. A modified genetic algorithm is used to select the sensitive rotary axis position and velocity combos; cubic spline interpolation is used to plan the axis motions based on the sensitive position and velocity combos.

Vorheriger Artikel Effect of welding heat input on pores in laser-arc hybrid welding of high nitrogen steel

Nächster Artikel Sensor data anomaly detection and correction for improving the life prediction of cutting tools in the slot milling process

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Titel: RTCP test axis motion planning for five-axis machine tool dynamic performance using observability optimization based on modified genetic algorithm
verfasst von: Qicheng Ding
Wei Wang
Jiexiong Ding
Jing Zhang
Chong Hu
Fengmin Lei
Li Du
Liping Wang
Publikationsdatum: 03.11.2021
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 1-2/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-08048-5

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