Prediction of modal parameters for thin-walled blade milling process considering material removal effect

Yu Li, Feng Ding, Weijun Tian, Dazhen Wang and Jinhua Zhou

PLOS ONE, 2025, vol. 20, issue 9, 1-23

Abstract: Accurate prediction of time-varying dynamic parameters during the milling process is a prerequisite for chatter-free cutting of thin-walled parts. In this paper, a matrix iterative prediction method based on weighted parameters is proposed for the time-varying structural modes during the milling of thin-walled blade structures. The thin-walled blade finite element model is established based on the 4-node plate element, and the time-varying dynamic parameters of the workpiece during the cutting process can be obtained by modifying the thickness of the nodes through the constructed mesh element finite element model It is not necessary to re-divide the mesh elements of the thin-walled parts at each cutting position, thus improving the calculation efficiency of the dynamic parameters of the workpiece. To further improve the prediction accuracy and efficiency of the finite element model, a three-layer neural network model is constructed, which takes the calculation results of the finite element model of the plate element as training samples to obtain the neural network model. The neural network model achieves a maximum prediction error of 2.02% compared to the finite element benchmark. Meanwhile, the training time of the three-layer neural network model is about 12 seconds. When the training model is used to batch calculate the dynamic parameters of the workpiece in different cutting stages, the loading time of the model and input data is about 1.2876s, and when the number of predicted cutting stage is 360, the prediction time is only 0.0039s. Using three-layer neural network model on the premise of ensuring the calculation accuracy can greatly improve the calculation efficiency.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0323871

DOI: 10.1371/journal.pone.0323871

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