熔融沉积成型有限元模拟与工艺优化研究
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摘要
熔融沉积成型(Fused Deposition Modeling,FDM)是一种近几十年发展起来的快速成型制造技术。随着现代工业的发展,熔融沉积成型被大量用在新产品研发、快速模具制作、医疗器械的设计开发和人体器官的原型制作等方面。熔融沉积成型成为快速成型制造技术的一个重要发展方向,然而,这种发展由于FDM成型零件的精度和强度较低而受到了限制。因此,本文研究熔融沉积成型数值模拟和工艺优化,以期提高FDM产品的精度,这不仅具有重要的理论意义,同时还具有重要的工程实用价值。
分析了熔融沉积成型产品精度的影响因素,它们分别是成型加工的机器误差、CAD模型误差、切片引起的误差、喷丝宽度引起的误差和材料的收缩引起的误差等,提出了提高熔融沉积成型产品零件精度的措施。
研究了熔融沉积成型温度场有限元数值模拟技术。在考虑了熔融沉积成型本身特点的基础上,作了合理的假设,建立了熔融沉积成型过程温度场的分析模型；在考虑了相变潜热和随温度变化的材料的热物性参数条件下,对熔融沉积成型的温场进行了模拟分析,得出了FDM温度场分布并作了具体分析,对于熔融沉积成型热过程理论分析和后续的研究有一定的参考价值。
研究了熔融沉积成型应力场有限元数值模拟技术。在基于FDM温度场的模拟结果的基础上,采用间接热力耦合的方法,建立了熔融沉积成型过程应力场的分析模型；应用APDL(ANSYS Parametric Design Language)语言编制了模拟程序,实现了对熔融沉积成型应力场的模拟,得出了一些有益的结论。
研究了基于小波神经网络的熔融沉积成型产品精度预测技术。分析了人工神经网络的原理和具体算法,在此基础上建立了基于BP神经网络的熔融沉积成型产品精度预测模型；在考虑到BP网络缺点的基础上,提出把小波分析理论和人工神经网络结合起来,克服了BP神经网络难以收敛和训练速度比较慢的一些缺点,建立了基于小波神经网络的熔融沉积成型产品精度预测模型,运用M语言编程,在MATLAB环境下编译,开发了基于小波神经网络的熔融沉积成型产品精度预测子系统,并进行了实际应用。
研究了熔融沉积成型工艺参数优化技术。分析了遗传算法及其基本操作,在基于小波神经网络的熔融沉积成型产品精度预测子系统的基础上,开发了基于遗传算法优化子系统,并针对熔融沉积成型进行了具体的工艺参数优化。
开发和完善了熔融沉积成型软件系统。在研究FDM快速成型的分层处理算法和智能化支撑添加算法的基础上,应用面向对象的语言VC++2005.net进行编程,在课题组成员前期研究的基础上,完善了熔融沉积成型软件系统,本软件可以对任意三维零件的STL模型进行切片处理和自动添加支撑,选择了数个零件模型,在转化为STL模型之后输入本软件系统,进行分层处理和添加支撑,最后输出CLI文件在快速成型机上进行了加工,结果证明本软件运行可靠,有投入实际生产的应用价值。
Fused deposition modeling (FDM) is one of the technologys on rapid prototyping and manufacturing (RPM) that develops in recent years. With the development of modern industry, fused deposition modeling is widely used in developing new product, rapid mold manufacting, design and development of medical devices, making prototype of human organs and so on. Fused deposition modeling becomes a important direction of development. However, the development is limited by the lower accuracy and intersity parts on FDM. Therefore, this paper studies the numerical simulation and process optimization in fused deposition modeling to improve the accuracy on FDM parts. It not only has important theoretical significance, but also has important practical value on engineering.
The influencing factors of the accuracy on FDM parts are analysed. They are the machine error on modeling and manufadting, the error on CAD model, the error on slicing, the error on spinneret width, the error on materials shrink and so on. This paper puts forward some methods which can improve the precision and surface quality of parts.
The finite element simulation technology of temperature field on FDM is studied. Based on the process characters of FDM, suitable hypothesis are introduced. The model of finite element simulation of temperature field on FDM is founded. Considering the latent heat of phase change and the theremal physical property parameters varied with the temperature's changing, the temperature field on FDM is analysed and simulated. The distribution of temperature field is obtained and the concrete analysis ars discussed. The conclusions have some referenced value on theoretical analysis of thermal process on FDM and following research.
The finite element simulation technology of stress field on FDM is studied. Based on the simulation of temperature field on FDM, the model of finite element simulation of stress field on FDM is founded through thermodynamic couple method indirectly. The simulation program is compiled by language APDL (ANSYS Parametric Design Language). The simulation analysis of stress field on FDM is completed and some profitable conclusion is given.
The parts'precision predidtion technology based on wavelet neural network (WNN) of stress field on FDM is studied. Based on analyzing the principle of artificial neural network and its concrete algorithm, the pretiction model of FDM parts is founded by BP neural network. Concidering the defect of BP neural network, the paper presents a new method that combines the wavelet with ANN in order to overcome some defect on ANN which convergence is difficult and its training speed is slow. Then, the prediction model of FDM parts based on wavelet neural network (WNN) is founded. The subsystem of FDM parts'prediction based on wavelet neural network (WNN) is developed by M language and compiled on MATLAB. It is practically applied on FDM.
The process parameter optimization technology based on genetic algorithm (GA) on FDM is studied. Based on analyzing the genetic algorithm (GA) and its basic operation and the subsystem of wavelet neural network (WNN) for FDM parts' prediction, the subsystem of genetic algorithm (GA) is developed. It is practically applied on FDM to optimize the concrete process parameters.
The software system of FDM is developed and perfected. Based on studying the algorithm of slicing and intelligent support on FDM, the software system of FDM is perfected by object oriented language VC++2005.net based on the earlier research work of our work team. The software can slice and add support automatic on any three-dimensional parts with STL format. Some parts are selected to test the software and the products were manufactured which were qualified. The results demonstrate that the software can run reliabilitily.
分析了熔融沉积成型产品精度的影响因素,它们分别是成型加工的机器误差、CAD模型误差、切片引起的误差、喷丝宽度引起的误差和材料的收缩引起的误差等,提出了提高熔融沉积成型产品零件精度的措施。
研究了熔融沉积成型温度场有限元数值模拟技术。在考虑了熔融沉积成型本身特点的基础上,作了合理的假设,建立了熔融沉积成型过程温度场的分析模型；在考虑了相变潜热和随温度变化的材料的热物性参数条件下,对熔融沉积成型的温场进行了模拟分析,得出了FDM温度场分布并作了具体分析,对于熔融沉积成型热过程理论分析和后续的研究有一定的参考价值。
研究了熔融沉积成型应力场有限元数值模拟技术。在基于FDM温度场的模拟结果的基础上,采用间接热力耦合的方法,建立了熔融沉积成型过程应力场的分析模型；应用APDL(ANSYS Parametric Design Language)语言编制了模拟程序,实现了对熔融沉积成型应力场的模拟,得出了一些有益的结论。
研究了基于小波神经网络的熔融沉积成型产品精度预测技术。分析了人工神经网络的原理和具体算法,在此基础上建立了基于BP神经网络的熔融沉积成型产品精度预测模型；在考虑到BP网络缺点的基础上,提出把小波分析理论和人工神经网络结合起来,克服了BP神经网络难以收敛和训练速度比较慢的一些缺点,建立了基于小波神经网络的熔融沉积成型产品精度预测模型,运用M语言编程,在MATLAB环境下编译,开发了基于小波神经网络的熔融沉积成型产品精度预测子系统,并进行了实际应用。
研究了熔融沉积成型工艺参数优化技术。分析了遗传算法及其基本操作,在基于小波神经网络的熔融沉积成型产品精度预测子系统的基础上,开发了基于遗传算法优化子系统,并针对熔融沉积成型进行了具体的工艺参数优化。
开发和完善了熔融沉积成型软件系统。在研究FDM快速成型的分层处理算法和智能化支撑添加算法的基础上,应用面向对象的语言VC++2005.net进行编程,在课题组成员前期研究的基础上,完善了熔融沉积成型软件系统,本软件可以对任意三维零件的STL模型进行切片处理和自动添加支撑,选择了数个零件模型,在转化为STL模型之后输入本软件系统,进行分层处理和添加支撑,最后输出CLI文件在快速成型机上进行了加工,结果证明本软件运行可靠,有投入实际生产的应用价值。
Fused deposition modeling (FDM) is one of the technologys on rapid prototyping and manufacturing (RPM) that develops in recent years. With the development of modern industry, fused deposition modeling is widely used in developing new product, rapid mold manufacting, design and development of medical devices, making prototype of human organs and so on. Fused deposition modeling becomes a important direction of development. However, the development is limited by the lower accuracy and intersity parts on FDM. Therefore, this paper studies the numerical simulation and process optimization in fused deposition modeling to improve the accuracy on FDM parts. It not only has important theoretical significance, but also has important practical value on engineering.
The influencing factors of the accuracy on FDM parts are analysed. They are the machine error on modeling and manufadting, the error on CAD model, the error on slicing, the error on spinneret width, the error on materials shrink and so on. This paper puts forward some methods which can improve the precision and surface quality of parts.
The finite element simulation technology of temperature field on FDM is studied. Based on the process characters of FDM, suitable hypothesis are introduced. The model of finite element simulation of temperature field on FDM is founded. Considering the latent heat of phase change and the theremal physical property parameters varied with the temperature's changing, the temperature field on FDM is analysed and simulated. The distribution of temperature field is obtained and the concrete analysis ars discussed. The conclusions have some referenced value on theoretical analysis of thermal process on FDM and following research.
The finite element simulation technology of stress field on FDM is studied. Based on the simulation of temperature field on FDM, the model of finite element simulation of stress field on FDM is founded through thermodynamic couple method indirectly. The simulation program is compiled by language APDL (ANSYS Parametric Design Language). The simulation analysis of stress field on FDM is completed and some profitable conclusion is given.
The parts'precision predidtion technology based on wavelet neural network (WNN) of stress field on FDM is studied. Based on analyzing the principle of artificial neural network and its concrete algorithm, the pretiction model of FDM parts is founded by BP neural network. Concidering the defect of BP neural network, the paper presents a new method that combines the wavelet with ANN in order to overcome some defect on ANN which convergence is difficult and its training speed is slow. Then, the prediction model of FDM parts based on wavelet neural network (WNN) is founded. The subsystem of FDM parts'prediction based on wavelet neural network (WNN) is developed by M language and compiled on MATLAB. It is practically applied on FDM.
The process parameter optimization technology based on genetic algorithm (GA) on FDM is studied. Based on analyzing the genetic algorithm (GA) and its basic operation and the subsystem of wavelet neural network (WNN) for FDM parts' prediction, the subsystem of genetic algorithm (GA) is developed. It is practically applied on FDM to optimize the concrete process parameters.
The software system of FDM is developed and perfected. Based on studying the algorithm of slicing and intelligent support on FDM, the software system of FDM is perfected by object oriented language VC++2005.net based on the earlier research work of our work team. The software can slice and add support automatic on any three-dimensional parts with STL format. Some parts are selected to test the software and the products were manufactured which were qualified. The results demonstrate that the software can run reliabilitily.
引文
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