文章摘要
选区激光烧结收缩率预测及工艺参数优化
Shrinkage Prediction Model for Parameters Optimization of the Selective Laser Sintering Process
  
DOI:10.16865/j.cnki.1000-7555.2018.06.020
中文关键词: 激光烧结  BP神经网络  支持向量回归  粒子群  收缩率
英文关键词: selective laser sintering  back propagation network  support vector regression  particle swarm optimization  contraction percentage
基金项目:国家自然科学基金资助项目(71601009)
作者单位
贺可太1, 刘 硕2, 陈哲涵1, 杨 智2 1. 北京科技大学 机械工程学院北京 100083
2. 中国航天科工集团第二研究院七Ο六所北京 100854 
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中文摘要:
      针对选区激光烧结过程中收缩变形问题,采用正交实验与测量的方法获得训练样本,分别应用BP神经网络与基于遗传算法优化的支持向量回归算法(GA-SVR),建立了针对聚苯乙烯(PS)材料的工艺参数与收缩率之间的定量预测模型,进一步应用自适应变异的粒子群算法对定量模型进行参数寻优。结果表明,基于相同的训练样本,GA-SVR算法相比BP神经网络来说拥有好的预测性能,在此基础上应用粒子群算法寻优得到了预热温度85 ℃、激光功率19.8 W、扫描速度2590 mm/s、铺粉层厚 0.1 mm、支撑厚度1 mm的最优工艺参数组合。模型可以更加准确地控制实际生产中收缩变形现象的产生,为烧结过程中优化控制提供了新思路。
英文摘要:
      In regard to the contraction problem in the process of selective laser sintering, orthogonal experiment and measuring methods were applied to acquire the training samples, then back propagation neural network or support vector regression optimized based on genetic algorithm (GA-SVR) was used to establish a quantifiable model between technical parameters and contraction rate, and to go much further to make parameter optimization for the quantifiable model by using self-adaptive variant particle swarm optimization. Based on same training sample, the result shows that GA-SVR perform forecasts better than does back propagation neural network, thereby by using particle swarm optimization to optimize parameter we get a best parameter combination by preheating temperature 85 ℃, laser power 19.8 W, scanning velocity 2590 mm/s, powder coating thickness 0.1 mm, wall thickness 1 mm. The model can be more accurate to control the contraction phenomena during practical manufacturing, and provides a new idea for optimizing control in the process of selective laser sintering.
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