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基于神经网络和遗传算法的熔融沉积成型多目标优化

Multiobjective Optimization of Fused Deposition Modeling Based on Neural Network and Genetic Algorithm

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【作者】 纪良波周天瑞钟雪华

【Author】 JI Liangbo1,2,ZHOU Tianrui1,ZHONG Xuehua1(1.School of Mechanical & Electronic Engineering,Nanchang University,Nanchang 330031,China;2.School of Mechanical & Materials Engineering,Jiujiang University,Jiujiang 332005,China)

【机构】 南昌大学机电工程学院九江学院机械与材料工程学院

【摘要】 在建立了熔融沉积成型工艺参数和目标函数的基础上,论述了人工神经网络和遗传算法在熔融沉积快速成型工艺参数优化中的应用。首先利用人工神经网络建立熔融沉积快速成型工艺参数与成型件尺寸精度、翘曲变形之间关系的数学模型,然后用遗传算法对工艺参数优化。根据多目标函数优化问题的单目标化思想,对优化后的单目标进行了分解,得到最优工艺参数条件下的成型件尺寸精度、翘曲变形,从而为建立和控制熔融沉积快速成型工艺参数提供了一种行之有效的途径。

【Abstract】 Based on the construction of the process parameters and objective function of fused deposition modeling,the application of aritificial neural network and genetic algorithm for processing optimization of fused deposition modeling was discussed.First of all,the mathematics model between the process parameters of fused deposition modeling and the precision and warpage of the part was set up with neural network.Then,the process parameters were optimized with genetic algorithm.According to the idea of converting the multiobjective optimization problem into the single-objective one,the optimal single-objective was decomposed and the precision and warpage of the part under the optimal condition was obtained.So,it is an effectual means for setting up and controlling parameters of modeling.

【基金】 江西省科技支撑项目(2007BG09604);南昌大学学科交叉基金项目(ncu0718)
  • 【文献出处】 热加工工艺 ,Hot Working Technology , 编辑部邮箱 ,2010年09期
  • 【分类号】TH161
  • 【被引频次】11
  • 【下载频次】318
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