An adaptive processing model for large superalloy pipeline components
Many large pipeline components in an aerospace engine are made of superalloy. Precision machining of superalloy curved surface components is an important way to achieve the weight reduction of the aerospace engine’s gas pipeline system. However, this type of components often uses the hot forging tec...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
EDP Sciences
2024-10-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2024/05/jnwpu2024425p801/jnwpu2024425p801.html |
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| Summary: | Many large pipeline components in an aerospace engine are made of superalloy. Precision machining of superalloy curved surface components is an important way to achieve the weight reduction of the aerospace engine’s gas pipeline system. However, this type of components often uses the hot forging technique to process blanks, and the components processed with the technique have large deformation and difficulties in cutting, which jointly exacerbate the complexity of mechanical processing. Domestic and foreign scholars have conducted fruitful theoretical and experimental study of adaptive machining theory, curved surface path planning algorithms and model reconstruction methods. At present, the theoretical analysis methods for deformation analysis and error compensation in the adaptive machining of high-temperature alloys are not clear; The precise physical model reconstruction technology is not yet mature and engineering applications are not yet popularized. Therefore, this article proposes a precise registration algorithm for the mechanical processing of superalloy pipeline blanks after forging and forming based on inner curved surface. Combined with digital measurement techniques of curved surfaces, it achieves the adaptive processing model reconstruction of large pipeline components. The technical feasibility is verified through curved surface model reconstruction and adaptive processing experiments. |
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| ISSN: | 1000-2758 2609-7125 |