EI、Scopus 收录
中文核心期刊
Wang Hongyue, Liu Yanfang, Du Desong, Qi Naiming. Research on gas flow characteristics of bernoulli gripper based on machine learning. Chinese Journal of Theoretical and Applied Mechanics, 2024, 54(8): 1-14. DOI: 10.6052/0459-1879-24-116
Citation: Wang Hongyue, Liu Yanfang, Du Desong, Qi Naiming. Research on gas flow characteristics of bernoulli gripper based on machine learning. Chinese Journal of Theoretical and Applied Mechanics, 2024, 54(8): 1-14. DOI: 10.6052/0459-1879-24-116

RESEARCH ON GAS FLOW CHARACTERISTICS OF BERNOULLI GRIPPER BASED ON MACHINE LEARNING

  • The non-contact transport of electronic components is one of the bottlenecks in achieving its fully automated production. Bernoulli gripper, a non-contact pneumatic gripper device, is widely used for gripping, positioning, and transporting objects in industry. However, the miniature Bernoulli gripper, which is used for clean and non-destructive transport of millimeter-scale semiconductor devices, has rarely been studied. In this study, combining the SST k-ω and laminar/turbulent turning γ models, the effects of the gripping gap, nozzle diameter, gripper size, and gas supply pressure on the suction force and gas consumption rate are first explored and the variation of the gas flow characteristics inside them is analysed. Then, based on hundreds of data obtained from numerical simulations as a dataset, six gripper performance prediction models were established using machine learning methods, and the hyper-parameters of the machine learning models were optimised by combining them with the corresponding intelligent algorithms. Finally, feature correlation analysis and comparison of the prediction effect of each machine learning model were conducted. The results show that the holding force is influenced by multiple factors: it grows with the gas supply pressure and the gripper size, while it initially rises and then declines with the gripping gap and the nozzle diameter; The gas consumption rate grows with the gas supply pressure, the gripping gap and the nozzle diameter. The optimal machine learning model has good prediction ability for holding force and gas consumption rate with accuracy evaluation metrics R2 around 0.95 and 0.97 (the closer to 1 the more accurate), respectively. The model prediction results for maximum holding force and corresponding gripper parameters at different gripper sizes have less than 5 % error compared to the numerical simulation results. In addition, it was found that the gas supply pressure and the clamping gap should be controlled at 3 ~ 5 bar and 0.045 ~ 0.08 mm, respectively, for low G-F factor operation of the gripper (gas consumption rate required to generate a unit of holding power g/s·N), and the model is used for prediction on holding force and gas consumption rate of the Bernoulli gripper under the influence of multiple parameters. The results of this paper can provide a reference for the design of miniature Bernoulli grippers and the optimisation of structural parameters.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return