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Intelligent computation modeling and analysis of a gripper for advanced manufacturing application

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Abstract

This paper is aimed to provide a new design modeling of the gripper via using the intelligent computing model. The gripper is designed to get benefit of a symmetric structure and compliant mechanism that can manipulate the objects with a stable force. The numerically experimental samples for the gripper are built and the finite element simulations are implemented. The displacement of left hand is collected. An intelligent computing model is formulated via a hybridization of the teaching learning optimization and feed forward neural network. The teaching learning optimization algorithm is embedded into neural network to enhance the training process. The results determined that the mean square error values of the entire model, the training, the testing, and validating are about 6.04e-07, 6.11e-07, 6.50e-08, and 1.10e-06, respectively. Furthermore, the coefficient of determination value of the entire model, the training, the testing, and validating are 0.9975, 0.9970, 0.9998, and 0.9677, accordingly. In addition, the proposed intelligent predictor is outperformed other regression methods such as linear regression, full 2nd order polynomial regression, and traditional artificial neural network. Moreover, the errors among the estimated from the proposed intelligent method and the prediction errors are less than 3%. It revealed that the proposed intelligent methodology is a well-suitable predictor for modeling the behaviors of gripper. The gripper is capable of providing a displacement amplification ratio of 2.85 and a max grasping force of 145.96 N. The gripper is potential for many practical applications such as robotics and manipulators in agricultural and electrics engineering.

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Acknowledgements

This research is supported by Industrial University of Ho Chi Minh City (IUH) under grant number 01/HD-DHCN.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Duc Nam Nguyen

  2. Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam

    Minh Phung Dang

  3. Faculty of Mechanical Engineering, Ho Chi Minh City Industry and Trade College, Ho Chi Minh City, Vietnam

    Tan Thang Nguyen

  4. Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thanh-Phong Dao

  5. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thanh-Phong Dao

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  1. Duc Nam Nguyen
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Correspondence to Thanh-Phong Dao.

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Nguyen, D.N., Dang, M.P., Nguyen, T.T. et al. Intelligent computation modeling and analysis of a gripper for advanced manufacturing application. Int J Interact Des Manuf 17, 2185–2195 (2023). https://doi.org/10.1007/s12008-022-00885-2

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  • DOI: https://doi.org/10.1007/s12008-022-00885-2

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