基于智能算法的光纤预制棒芯层制备工艺参数优化

Optimization of manufacturing parameters for optical fiber preform core based on intelligent algorithm

结合BP(back propagation)神经网络和遗传算法,提出一种高质量低成本工艺参数优化方法。选取轴向气相沉积法(vapor axial deposition,VAD)制备光纤预制棒芯层时喷灯气体(H_(2)-1、H_(2)-2、H_(2)-3、Ar-1、Ar-2、Ar-3、O_(2)-1、O_(2)-2、SiCl_(4))流量作为输入变量,制备出的光纤预制棒芯层的质量作为输出变量,建立神经网络模型;将训练好的神经网络模型与具有全局寻优能力的遗传算法相结合,以高质量等级光纤预制棒芯层为寻优目标,优化得到高质量等级气体参数;对得到的参数以低成本为目标进行寻优,得到高质量低成本工艺参数。试验结果表明,与优化前的人工优化结果相比,制备出的光纤预制棒芯层达到较高质量的同时成本降低22.19%。

Combined with back propagation(BP)neural network and genetic algorithm,a high-quality and low-cost process parameter optimization method was proposed.The flow rate of two blowtorch gases(H_(2)-1,H_(2)-2,H_(2)-3,Ar-1,Ar-2,Ar-3,O_(2)-1,O_(2)-2,SiCl_(4))during the preparation of optical fiber preform core layer by vapor axial deposition(VAD)was selected as the input variable.The quality of the prepared optical fiber preform core layer was taken as the output variable in the established neural network model.The trained neural network model was combined with the genetic algorithm with global optimization ability,and the high quality core layer of optical fiber preform was taken as the optimization objective to obtain high quality gas parameters.The obtained parameters were selected at low cost,and the high quality and low cost process parameters were obtained.The experimental results showed that compared with the manual optimization results before optimization,the prepared optical fiber preform core layer met the high quality requirements and the cost was reduced by 22.19%.