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Optimization of milling parameters based on GA-BP neural network

Since the selection of machining parameters usually relies on the experience of workers or traditional calculation formulas, the traditional prediction method has the disadvantages of a complicated arithmetic process, large prediction deviation, and high consumption cost, making it challenging to me...

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Bibliographic Details
Published in:Journal of physics. Conference series 2024-08, Vol.2815 (1), p.12052
Main Authors: Miao, Liqin, Liao, Chaoneng, Zhang, Dashun, Liang, Huaidan, Gao, Di
Format: Article
Language:English
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Summary:Since the selection of machining parameters usually relies on the experience of workers or traditional calculation formulas, the traditional prediction method has the disadvantages of a complicated arithmetic process, large prediction deviation, and high consumption cost, making it challenging to meet the increasing demand of production and processing. Therefore, this paper proposes a machining quality prediction model based on the GA-BP neural network. Through experiments, it verifies the data-fitting ability of the prediction model and then takes the prediction model as the optimization objective, culminating in a multi-objective optimization model for process parameters based on the NSGA-II algorithm. Experiments demonstrate that the cutting force and surface roughness obtained by the optimization model are 3.6% and 10.0% lower than those obtained by the empirical parameters, respectively, leading to reductions of 3.6% and 10.6%, which verified the optimization effect of the model.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2815/1/012052