三维成型系统的细分驱动及温度预测研究

发布时间：2018-01-27 06:16

本文关键词： 三维成型细分驱动成型喷头温度 QPSO-BP算法　出处：《西安科技大学》2017年硕士论文　论文类型：学位论文

【摘要】：目前三维成型技术被世界各国人所接受,并且这种将数字化模型文件快速地转化成生物材料或光敏树脂等的技术也迅速发展,前景十分广阔。桌面级的三维成型机具有耗材广泛、设备要求低、操作方便等优点。但是三维成型机的硬件主要包括电机、成型喷头、主控器等,其内部结构复杂,电机的稳定运行和温度的精确控制是确保成型物品精度的重要环节。所以,对如何提高三维成型机的成型精度的研究具有重要的意义。本文对自主研发的三维成型机展开研究,首先,设计出三维成型机的整体系统方案及硬件结构,侧重研究系统的软硬件平台和部分内部资源在三维成型机中的具体应用,同时给出三维成型系统的整体工作链和控制原理。然后重点分析步进电机驱动系统,包括细分驱动技术的概念、特点和原理。除此之外,针对步进电机步进电机失步或跳步、和易产生共振等不足,本文提出电流矢量恒幅均匀旋转的细分驱动方法,该方法采用保持电流合成矢量的幅值不变,均匀地变化旋转的角度的思路,使得电流矢量恒幅且均匀细分驱动。经实验仿真验证,并与常用的细分驱动方法对比,该方法在三维成型系统上的应用是可行性,不仅能使得系统中的五轴电机协调稳定运行,而且成型精度有所提高。其次,成型喷头温度的高低直接影响熔融耗材的成型实物效果。针对三维成型机的成型喷头温度波动大对成型效果甚至喷头寿命的影响,提出基于QPSO-BP神经网络建立成型喷头温度预测模型,该方法采用取长补短的优化思路,利用量子粒子群优化BP神经网络的初始权值和阈值,来提前预测温度趋势以达到对温度精确控制的目的。通过Matlab仿真证明了应用该算法的控制效果。最后,在软件设计及实验性测试方面,对三维成型过程中所用的建模软件、切片软件进行分析,并设计操控界面。成型的上下位机进行调试后,在自主研制的平台上实际加工猎人头像实物,对本文提出的两种方法进行实验验证。实验测试及分析表明,优化后比优化前的成型实物的粗糙度降低了 0.3mm。本文提出的方法可以为精细加工的成型系统控制提供参考。
[Abstract]:At present, 3D molding technology is accepted by people all over the world, and the technology of converting digitized model files into biomaterials or Guang Min resin is also developing rapidly. Desktop three-dimensional molding machine has a wide range of materials, low requirements for equipment, easy to operate and so on. But the hardware of three-dimensional molding machine mainly includes motor, molding nozzle, main controller and so on. Its internal structure is complex, the stable operation of the motor and the precise control of temperature are the important links to ensure the precision of the molding products. It is of great significance to study how to improve the forming accuracy of 3D molding machine. Firstly, the whole system scheme and hardware structure of 3D molding machine are designed. Focus on the hardware and software platform of the system and part of the internal resources of the specific application in the three-dimensional molding machine, at the same time, give the overall working chain and control principle of the three-dimensional molding system, and then focus on the analysis of the stepping motor drive system. It includes the concept, characteristics and principle of the subdivision drive technology. In addition, it aims at the shortcomings of stepping motor, such as out of step or jump, and easy to produce resonance. In this paper, a subdivision drive method for constant amplitude uniform rotation of current vector is proposed. The method adopts the idea of keeping the amplitude of the current synthesis vector unchanged and changing the rotation angle uniformly. Make the current vector constant amplitude and uniform subdivision drive. The simulation results show that this method is feasible in 3D molding system compared with the usual subdivision drive method. It can not only make the five-axis motor in the system run harmoniously and stably, but also improve the forming accuracy. Secondly. The temperature of the molding nozzle has a direct effect on the material object effect of melt consumption. The temperature fluctuation of the molding nozzle has a great effect on the forming effect and even the life of the nozzle for the three-dimensional molding machine. Based on QPSO-BP neural network, the temperature prediction model of shaping nozzle is established. The optimization method uses quantum particle swarm optimization (QPSO) to optimize the initial weight and threshold of BP neural network. In order to predict the temperature trend in advance to achieve the purpose of accurate temperature control. The control effect of the algorithm is proved by Matlab simulation. Finally, in software design and experimental testing. The modeling software and slicing software used in the process of 3D molding are analyzed and the control interface is designed. After debugging the molding upper and lower machine the hunter head is actually processed on the self-developed platform. The experimental results show that the two methods proposed in this paper are verified by experiments and analyses. The roughness of the object after optimization is 0.3 mm lower than that before optimization. The method proposed in this paper can provide a reference for the control of the forming system of fine machining.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TP311.52;TH16

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