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王洪悦, 刘延芳, 杜德嵩, 齐乃明. 基于机器学习的伯努利夹持器气体流动特性研究. 力学学报, 待出版. DOI: 10.6052/0459-1879-24-116
引用本文: 王洪悦, 刘延芳, 杜德嵩, 齐乃明. 基于机器学习的伯努利夹持器气体流动特性研究. 力学学报, 待出版. DOI: 10.6052/0459-1879-24-116
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, in press. 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, in press. DOI: 10.6052/0459-1879-24-116

基于机器学习的伯努利夹持器气体流动特性研究

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

  • 摘要: 电子元器件的非接触输运是实现其全自动化生产的瓶颈之一. 伯努利夹持器作为一种非接触式气动夹持装置被广泛应用于工业中物体的抓取、定位和运输, 而适用于mm级别半导体器件清洁无损运输的微型伯努利夹持器还鲜有研究. 文章采用SST k-ω并结合层流/湍流转捩γ模型, 探讨了夹持间隙、喷嘴直径、夹持器尺寸以及供气压力对吸持力和气体消耗率的影响, 并分析了其内部的气体流动特性变化; 以数值模拟获得的数百个数据作为数据集, 利用机器学习方法建立6个夹持器性能预测模型, 并结合相应的智能算法对机器学习模型的超参数进行调优, 进行特征相关性分析, 并比较了各机器学习模型的预测效果. 结果表明, 吸持力受多因素综合影响: 其随供气压力、夹持器尺寸的增大而增大, 随夹持间隙及喷嘴直径的增大先增大后减小; 气体消耗率随供气压力、夹持间隙以及喷嘴直径的增大而增大. 最佳的机器学习模型对吸持力和气体消耗率的预测准确率评价指标R2(越接近1越准确)分别在0.95和0.97左右, 预测了不同夹持器尺寸下的最大吸持力及所对应的夹持器参数, 与数值结果进行比较其误差小于5%, 具有良好的预测能力. 此外发现, 为保证夹持器低G-F因子工作(产生单位吸持力所需的气体消耗率g/s·N)其供气压力和夹持间隙应分别控制在3 ~ 5 bar和0.045 ~ 0.08 mm之间, 模型可用于多参数影响下的伯努利夹持器吸持力和气体消耗率的预测. 结果可为微型伯努利夹持器设计及结构参数优化提供参考.

     

    Abstract: 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.

     

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