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Process parameters design of squeeze casting through SMR ensemble model and ACO

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Abstract

Process parameters are key to the production and cast quality of squeeze casting (SC). The exiting methods to obtain the process parameters are selected directly based on experimental results or based on the optimization through single models, which require more experiments, are expensive, time-consuming, and less adaptable. In this study, a process-parameter design method of SC based on ensemble learning is proposed, and a design and optimization framework for SC process parameters based on multi-model ensemble is established. Based on this framework, using the support vector machine (SVM), multivariate linear regression (MLR), and random forest (RF) model as the unit models, and an improved model assembling strategy (R2 weight assignment), the ensemble model (SMR) for optimizing the SC process parameters is established. Then, to obtain the optimal SC process parameters, the ant-colony-optimization (ACO) algorithm is adopted to solve the SMR model. The two application cases show that the proposed ensemble strategy is reasonable and effective; the ensemble model had higher prediction accuracy and stronger generalization ability, even in small data sample situations. Furthermore, it effectively improved the quality of cast by its designed process parameters, and the shrinkage porosity of Case 1 cast, using the designed process parameters, was reduced to 1.316%. Compared with the conventional methods for designing SC process parameters, the method based on the ensemble model is more efficient and accurate, and reduces the cost.

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The raw/processed data required cannot be shared at this time as the data also forms part of an ongoing study.

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The custom code required to reproduce these findings cannot be shared at this time as the code also forms part of an ongoing study.

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Acknowledgements

We would like to thank Editage (http://www.editage.cn) for English language editing.

Funding

This work was supported by the National Natural Science Foundation of China (grant no. 51965006, 51875209), Guangxi Natural Science Foundation (grant no. 2018GXNSFAA050111), and the Open Fund of National Engineering Research Center of Near-Shape Forming for Metallic Materials, the Key Laboratory of High Efficient Near-Net-Shape Forming Technology and Equipments for Metallic Materials (Ministry of Education) (grant no. 2019001), and 2022 China -ASEAN Information Harbor Project (grant No.7).

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Authors and Affiliations

  1. Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology, Guangxi University, Nanning, 530003, China

    Jianxin Deng & Ling Wang

  2. School of Mechanical Engineering, Guangxi University, No.100 Daxue Road, Nanning, 530003, China

    Jianxin Deng, Ling Wang, Gang Liu, Xiusong Wu & Jiawei Liang

  3. National Engineering Research Center of Near-Net-Shape Forming for Metallic Materials, South China University of Technology, Guangzhou, 510640, China

    Dongdong You

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  1. Jianxin Deng
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Contributions

Jianxin Deng: conceptualization, methodology, formal analysis, writing—original draft, writing—review & editing, supervision, project administration, funding acquisition. Ling Wang: methodology, software, validation, investigation, formal analysis, resources, data curation, writing-original draft, writing-review & editing, visualization. Gang Liu: Resources. Dongdong You: conceptualization. XiuSong Wu: Resources. JiaWei Liang: resources.

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Correspondence to Jianxin Deng.

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Deng, J., Wang, L., Liu, G. et al. Process parameters design of squeeze casting through SMR ensemble model and ACO. Int J Adv Manuf Technol 130, 2687–2704 (2024). https://doi.org/10.1007/s00170-023-12805-z

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