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人工神经网络在激光表面强化控制上的应用

Controlling laser surface strengthening process based on artificial neural network

  • 摘要: 通过大量试验,分析了使用不同的激光工作参数对材料进行激光强化处理时,所得材料表面可归为 4种类别,即:未相变硬化,相变硬化,表面微熔及表面熔凝。并建立了激光工作参数与材料表面强化类别之间关系的人工神经网络模型。实验表明,运用该模型可以方便、准确的选择激光工艺参数,控制材料表面强化类别及工作性能。

     

    Abstract: Experiments show that metal surface properties may produce four results through laser strengthening treatment,i.e.non-transformation hardening,transformation hardening,shallow melt and melt.After analysis,the relationships between the four classifications and laser processing parameters,such as laser power,laser processing beam diameter,laser scanning velocity,have been established based on artificial neural network.HT300 experiments show that laser processing parameters can be chosen conveniently and material surface quality can be controlled effectively.

     

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