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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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Published in: | Journal of physics. Conference series 2024-08, Vol.2815 (1), p.12052 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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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. |
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ISSN: | 1742-6588 1742-6596 |
DOI: | 10.1088/1742-6596/2815/1/012052 |