高速走丝电火花线切割机床电极丝振动控制的模拟与仿真

发布时间：2018-03-20 19:31

本文选题：高速往复走丝　切入点：电极丝　出处：《青岛理工大学》2016年硕士论文　论文类型：学位论文

【摘要】：高速往复走丝电火花线切割机床是我国自主研发的,在我国的制造领域具有极其重要的地位,已经在模具制造和精密零件加工等领域得到广泛的应用。但由于在加工过程中电极丝的振动问题,制约了机床在加工速度和表面粗糙度上的进一步提升,为此电极丝振动控制装置应运而生。本文首先介绍了高速往复走丝电火花线切割机床的研究现状以及相关理论,为后续的研究打好了基础。同时设计了一种电极丝振动控制装置,由振动检测部分和张丝部分等组成,它能够根据实际的加工情况自动地调节电极丝的松紧,减少了人为因素的干扰。利用装有这种装置的北京凝华NH系列线切割机床NH7740B进行加工实验,通过实验数据的对比,证明了该装置能够较大幅度的提升加工速度和降低工件的表面粗糙度。但对于装有电极丝振动控制的高速往复走丝电火花线切割机床而言,原有的加工参数已经不再适合,不能充分的利用该装置,为了更好地利用和普及这些性能更加优良的机床,需要找到与之相匹配的加工参数,以此来指导实际生产。为了能够找到与之相匹配的加工参数,需要进行建模与仿真,为此决定使用BP神经网络。因为BP神经网络是整个人工神经网络的核心,最精华的部分,具有很强的学习和适应能力,能够处理非线性关系的数据,找出其存在的潜在关系,此外还具有很强的容错能力。建立加工工艺预测模型和加工参数优化模型,通过BP神经网络进行加工工艺的预测以及加工参数的优化。通过优化所得的加工参数进行加工实验,在加工速度和表面粗糙度方面大有提高,证明了模型的准确性,有利于电极丝振动装置的普及和发展。
[Abstract]:High speed reciprocating wire WEDM machine tool is independently developed in our country, and has an extremely important position in the field of manufacturing in our country. It has been widely used in die manufacturing and precision parts machining. However, the vibration of electrode wire in machining process restricts the further improvement of machining speed and surface roughness of machine tools. For this reason, the electrode wire vibration control device came into being. Firstly, this paper introduces the research status and related theory of high-speed reciprocating wire WEDM machine tool. At the same time, a vibration control device of electrode wire is designed, which is composed of vibration detection part and tensioning part, which can automatically adjust the tension of electrode wire according to the actual processing conditions. The interference of human factors was reduced. The processing experiment was carried out by using the NH7740B, a series of wire cutting machine tools of Beijing Inferon NH series equipped with this device, and the experimental data were compared with each other. It is proved that the device can greatly increase the machining speed and reduce the surface roughness of the workpiece, but the original machining parameters are no longer suitable for the high speed reciprocating wire WEDM machine which is controlled by the vibration of the electrode wire. Unable to make full use of this device, in order to make better use of and popularize these machine tools with better performance, we need to find corresponding processing parameters to guide actual production. We need to model and simulate, so we decided to use BP neural network, because BP neural network is the core of the whole artificial neural network. In addition, it has strong fault-tolerant ability. The prediction model of machining process and the optimization model of machining parameters are established. The prediction of machining process and the optimization of machining parameters are carried out by BP neural network, and the machining speed and surface roughness are greatly improved by optimizing the processing parameters, which proves the accuracy of the model. It is beneficial to the popularization and development of electrode wire vibration device.
【学位授予单位】：青岛理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TG484

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