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Prediction and Optimization of Robotic Machining Grinding Force based on Neural Network-Genetic Algorithm[J]. Diamond & Abrasives Engineering. doi: 10.13394/j.cnki.jgszz.2024.0045
Citation: Prediction and Optimization of Robotic Machining Grinding Force based on Neural Network-Genetic Algorithm[J]. Diamond & Abrasives Engineering. doi: 10.13394/j.cnki.jgszz.2024.0045

Prediction and Optimization of Robotic Machining Grinding Force based on Neural Network-Genetic Algorithm

doi: 10.13394/j.cnki.jgszz.2024.0045
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  • Received Date: 2024-03-12
  • Accepted Date: 2024-06-17
  • Rev Recd Date: 2024-05-27
  • Available Online: 2024-06-21
  • Objectives:In this paper, the grinding force signal of industrial robot processing sandstone is studied and analyzed, and the influence of processing parameters on the grinding force signal of different processing directions is explored, the prediction model of grinding force is established by BP neural network, and the optimization of grinding process parameters is carried out based on genetic algorithm with grinding force as constraint condition and material removal rate as objective function, it provides reference for the selection of technological parameters of industrial robot grinding process.Taking KUKA KR60L30HA industrial robot processing sandstone as an example, the grinding force prediction and grinding process parameter optimization were studied based on BP neural network and genetic algorithm.

    Methods: Firstly, the orthogonal test method is used to analyze the influence of the processing parameters on the grinding force. Secondly, the grinding force  model is trained and predicted based on BP neural network. Finally, the grinding process parameters are optimized by the genetic algorithm.

    Results:The conclusions are as follows: (1) The influence of grinding process parameters on the grinding component and grinding force is increasing with the increase of radial cutting depth ae, axial cutting depth ap and feed speed vw. With the increase of spindle speed n, the grinding force decreases. (2) The model established based on BP neural network has good prediction accuracy and stability, which meets the prediction requirements. (3)The optimized grinding process parameters obtained by genetic algorithm are radial tangential depth ae=2.01mm, axial tangential depth ap=2.59mm, spindle speed n=9910.37r/min and feed speed vw=3116.06mm/min, and the material removal rate is RMMR=16221.90mm³ / min.

    Conclusions:(1) the influence of grinding parameters on grinding components and grinding resultant force is different in the order of feed velocity vw, radial cutting depth AE and axial cutting depth AP. The grinding force increases with the increase of AE, AP and VW, and decreases with the increase of spindle speed N. (2) BP neural network is trained and forecasted with orthogonal experiment data. The neural network model has good precision and stability, and meets the requirement of prediction. (3) with the reciprocal of material removal rate RMMR as the objective optimization function, the process parameters were obtained as radial cutting depth AE = 2.17 mm, axial cutting depth AP = 1.54 mm, spindle speed n = 9909.52 r/min, feed speed VW = 3395.25 mm/min, the material removal rate rmmr = 11346.25 mm3/min.
     

     

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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