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Laser cutting quality prediction based on pareto genetic algorithm

机译:基于帕累托遗传算法的激光切割质量预测

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Prediction and optimization of cutting quality is an important method to improve the cutting quality. Aiming at the prediction of quality characteristic parameters for pulsed Nd: YAG laser cutting, a prediction algorithm based on pareto genetic algorithm is used in this paper. KW(Kerf Width) and MRR(Material removal rate) are selected as the optimization objective, and the multi-objective optimization model is established in this paper. The theoretical analysis and experimental results show that the algorithm can be used for KW and MRR prediction in pulsed Nd: YAG laser cutting. A large number of forecast data show the rules as follows. The effects of three types of combined parameters( gas pressure and pulse width, pulse width and pulse frequency, pulse width and cutting speed) on KW are obvious, while the effects of combined parameters, pulse width and pulse frequency, pulse frequency and cutting speed are more obvious on MRR. The study in this paper can provide theoretical guidance and parameters for prediction and optimization of quality in laser cutting.
机译:切割质量的预测和优化是提高切削质量的重要方法。旨在预测脉冲Nd的质量特性参数:YAG激光切割,本文使用了一种基于Pareto遗传算法的预测算法。选择KW(KERF宽度)和MRR(材料去除率)作为优化目标,并在本文中建立了多目标优化模型。理论分析和实验结果表明,该算法可用于脉冲Nd:YAG激光切割中的KW和MRR预测。大量预测数据显示如下规则。三种组合参数(气体压力和脉冲宽度,脉冲宽度,脉冲宽度和脉冲频率,脉冲宽度和脉冲频率,脉冲宽度和切割速度)的影响显而易见,而组合参数,脉冲宽度和脉冲频率,脉冲频率和切割速度的影响MRR更加明显。本文的研究可以为激光切割中的质量的预测和优化提供理论指导和参数。

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