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为了提高激光纳米陶瓷涂层工艺优化的精度,提出神经网络和遗传算法的激光纳米陶瓷涂层工艺优化模型。首先选择影响激光纳米陶瓷涂层的工艺参数,然后采用遗传算法对神经网络进行优化,并将优化神经网络用于建立工艺参数与结合强度和显微硬度之间的预测模型,最后的激光纳米陶瓷涂层工艺优化实验结果表明,本文模型降低了激光纳米陶瓷涂层工艺优化误差,可以准确对激光纳米陶瓷涂层的性能进行预测。
In order to improve the accuracy of laser nano-ceramic coating process optimization, a neural network and genetic algorithm laser nano-ceramic coating process optimization model is proposed. Firstly, the process parameters affecting the laser nano-ceramic coating are selected, and then the genetic algorithm is used to optimize the neural network. The optimized neural network is used to establish the prediction model between the process parameters and the bonding strength and microhardness. The final laser nano-ceramic The experimental results of the coating process optimization show that the model reduces the process optimization error of the laser nano-ceramic coating, and can accurately predict the performance of the laser nano-ceramic coating.