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Multiple clearance robustness optimization of a chain ramming machine based on a data-driven model

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Abstract

This paper provides a feasible scheme for robust optimization of the ramming process of a chain ramming machine with multiple clearances using a data-driven modeling framework based on deep neural networks. The forced ramming process of the chain ramming machine is studied, and a specially tailored experimental platform is demonstrated to validate the multiple clearances combined dynamic model of the ramming machine. In the model, the spatial clearances of the rollers in the track and sprocket teeth grooves and of the projectile in the conveying channel are described in detail for the first time. The displacements, velocities and lateral and vertical swing angles of the projectile when the projectile is forced in place with different combinations of clearances serve as the training dataset for the data-driven-forced ramming model. On this basis, the architecture of the deep neural network for the forced ramming process is designed by an integer optimization method to establish the corresponding data-driven model. Finally, a multiobjective robust optimization study is carried out under the data-driven model, and the optimization results with considering controllable and uncontrollable variance provide a reference for the project to improve the accuracy of ramming projectiles in place.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

\(F_{n}\) :

Normal contact force

\({\mathbf{F}}_{t}\) :

Tangential friction

\({\mathbf{P}}\) :

The array of independent joint variables

\({\mathbf{H}}\) :

Conversion matrix

\({\mathbf{M}}\) :

Mass matrix of the system

\({\mathbf{C}}\) :

Flexibility matrix of the system

\({\mathbf{w}}_{i}\) :

Weight matrix of the neural network

\({\mathbf{b}}_{i}\) :

Threshold matrix of the neural network

\( f_{A}(\cdot)\) :

Activation function

\(\delta\) :

Normal penetration depth of the contact point

\(\dot{\delta }\), \({\dot{\mathbf{\tau }}}\) :

Normal and tangential relative velocity

\(N_{h}\) :

The number of hidden layers

\(N_{{\varvec{n}}}\) :

The number of neurons each hidden layer

\({\mathbf{R}}^{2}\) :

The goodness-of-fit index

\({\mathbf{P}}_{\text{v}}\) :

The random variation in uncontrollable parameters

\({\mathbf{P}}_{{\varvec{c}}}\) :

The nonrandom variation uncontrollable parameters

\({\mathbf{X}}_{{\varvec{v}}}\) :

The random variation in controllable parameters

\({\mathbf{X}}_{{\varvec{c}}}\) :

The nonrandom controllable parameters.

\({\mathbf{u}}_{y}\), \({{\varvec{\upsigma}}}_{y}\) :

The mean and variance of the dynamic responses of the projectile

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 11472137) and the Fundamental Research Funds for the Central Universities (Grant Nos. 309181A8801 and 30919011204).

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 11472137) and the Fundamental Research Funds for the Central Universities (Grant Nos. 309181A8801 and 30919011204).

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Authors and Affiliations

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Yong Li, Linfang Qian, Guangsong Chen & Wenkuan Huang

  2. Northwest Institute of Mechanical and Electrical Engineering, Xianyang, 712099, Shanxi, China

    Linfang Qian

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Contributions

All authors contributed to the study conception and design. YL and LQ performed formula derivation, numerical calculation, and data collection. YL and WH performed data analyses. The first draft of the manuscript was written by YL. GC commented on and revised the previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Linfang Qian.

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Li, Y., Qian, L., Chen, G. et al. Multiple clearance robustness optimization of a chain ramming machine based on a data-driven model. Nonlinear Dyn 111, 13807–13828 (2023). https://doi.org/10.1007/s11071-023-08589-2

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