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基于PSO-BP算法的3D打印制件精度预测模型
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国家自然科学基金资助项目(51605346)


Accuracy Predictive Model of 3D Printing Parts Based on PSO-BP Algorithm
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    摘要:

    针对选择性激光烧结(SLS)中制件精度和工艺参数难以选择的问题以及BP神经网络本身缺陷,提出一种利用粒子群算法优化的BP神经网络建立SLS烧结件精度预测模型的方法。首先根据SLS成型工艺的特点,分析影响成型件精度的因素,通过实验获得不同激光功率、扫描速度、扫描间距和分层厚度条件下多组成型件精度数据,并采用多目标函数优化的单目标化思想优化目标函数,然后通过粒子群算法优化BP神经网络。用优化后的最优解作为BP神经网络算法的初始权值和阈值,利用MATLAB建立优化后的BP神经网络预测模型,对优化后的精度函数模型进行预测分析,并与传统BP神经网络获得的预测结果进行对比。结果表明:粒子群优化的神经网络模型具有良好的全局搜索能力和收敛性,精度预测更加准确,对SLS打印制件具有一定的指导作用。

    Abstract:

    For the problems of the parts accuracy and the technological parameters selection in selective laser sintering and the defects of BP neural network,a accuracy prediction model was proposed through the improved BP neutral network. Firstly, considering the characteristics of SLS process and factors affecting the accuracy of the parts, the accuracy data of multigroup parts under different laser powers, scanning speeds, scanning pitches and layer thicknesses were obtained through experiments, and the singleobjective idea based on multiobjective function optimization was used to deal with them. Then, the optimal solution obtained by particle swarm optimization was used as the initial weight and threshold of BP neural network. The optimized BP neural network prediction model established in MATLAB was used to predict the accuracy function model, and the prediction results were compared with those obtained by traditional BP neural network. The results show that the neural network model with particle swarm optimization has good global search ability and convergence, and its accuracy prediction is more accurate, and it has some practical guidance for SLS printed parts.

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王丹妮,周敏,高强,段现银.基于PSO-BP算法的3D打印制件精度预测模型[J].机床与液压,2019,47(16):1-5.
. Accuracy Predictive Model of 3D Printing Parts Based on PSO-BP Algorithm[J]. Machine Tool & Hydraulics,2019,47(16):1-5

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  • 在线发布日期: 2020-03-12
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