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Analysis and prediction of shrinkage cavity defects of a large stepped shaft in open-die composite extrusion based on machine learning

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Abstract

A stepped shaft, as an integral part of an aeroengine system, is prone to shrinkage cavity defects during open-die composite extrusion, which affects the service performance and life of the fan shaft. First, the deformation process of the fan shaft in open-die composite extrusion was analyzed using finite element simulation. The shrinkage primarily occurred in the forward extrusion stage. Subsequently, the mathematical conditions of the shrinkage cavity in the forward extrusion were obtained using the differential element method. The die parameters affecting the shrinkage cavity were mainly the die inclination and extrusion ratio. A finite element simulation of a simplified forward extrusion model and a machine learning classification algorithm were used to create a shrinkage cavity prediction diagram with solid performance and good generalization. Stepped-shaft forging with satisfactory performance and no shrinkage was obtained by selecting appropriate die parameters according to the shrinkage prediction diagram.

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Data availability

The data used in the analysis are obtained by combining the FEM method with the actual working conditions, and the specific data are not published according to the laboratory requirements.

Code availability

The code is fully available and performs well, but is not publicly available.

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Funding

This work was financially supported by Green Manufacturing System Integration Project of the Ministry of Industry and Information Technology (Grant No: 2018272106).

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Authors and Affiliations

  1. School of Materials Science and Engineering, Chongqing University, Chongqing, 400030, China

    Menghan Wang, Menglong Du & Songlin Li

  2. China National Erzhong Group Co., Deyang, 400015, Sichuan Province, China

    ZhouTian Wang

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  1. Menghan Wang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by ZhouTian Wang, Songlin Li, and Menglong Du. The first draft of the manuscript was written by Menglong Du and Menghan Wang; all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Menghan Wang.

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Wang, M., Du, M., Li, S. et al. Analysis and prediction of shrinkage cavity defects of a large stepped shaft in open-die composite extrusion based on machine learning. Int J Adv Manuf Technol 127, 2723–2735 (2023). https://doi.org/10.1007/s00170-023-11634-4

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  • DOI: https://doi.org/10.1007/s00170-023-11634-4

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