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Adaptive CNC machining process optimization of near-net-shaped blade based on machining error data flow control

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Abstract

This study proposes a high precision machining method of near-net-shaped blade based on multi-process machining errors data flow control. The multi-process machining geometric error and mechanical models of the blade multi-process are firstly constructed. The stiffness of blade-fixture system is secondly analyzed. The machining error flow of the blade multi-process is finally controlled by the adaptive CNC machining process under the sufficient stiffness of blade-fixture system. The results show that the dynamic displacement response of the blade multi-process is controlled within 0.007 mm. The optimized adaptive CNC machining process of the multi-process geometric machining error data flow control can realize the high-precision manufacturing of blade.

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Funding

This research was supported in part by Xi’an Aero-Engine (Group) Ltd. This research is supported in part by Project on the Integration of Industry, Education and Research of Jet Engine Corporation of China (HFZL2020CXY020).

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Authors and Affiliations

  1. State Key Laboratory of Tribology, Beijing Key Lab of Precision/Ultra-Precision Manufacturing Equipment and Control, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Dongbo Wu, Hongru Lv & Hui Wang

  2. Institute for Aero Engine, Tsinghua University, Beijing, 100084, China

    Dongbo Wu

  3. AECC Xi’an Aero-Engine LTD., Xi’an, 710021, Shaanxi, China

    Jie Yu

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  1. Dongbo Wu
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  2. Hongru Lv
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  3. Hui Wang
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  4. Jie Yu
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Contributions

Dongbo Wu: experiment, data analysis, original draft writing.

Hongru Lv: experiment and writing.

Hui Wang: methodology, formal analysis, writing—review and editing.

Jie Yu: experiment.

Corresponding author

Correspondence to Hui Wang.

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Wu, D., Lv, H., Wang, H. et al. Adaptive CNC machining process optimization of near-net-shaped blade based on machining error data flow control. Int J Adv Manuf Technol 124, 3257–3273 (2023). https://doi.org/10.1007/s00170-022-10705-2

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  • DOI: https://doi.org/10.1007/s00170-022-10705-2

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