高速走絲電火花線切割機床電極絲振動控制的模擬與仿真
本文選題:高速往復走絲 切入點:電極絲 出處:《青島理工大學》2016年碩士論文 論文類型:學位論文
【摘要】:高速往復走絲電火花線切割機床是我國自主研發(fā)的,在我國的制造領域具有極其重要的地位,已經在模具制造和精密零件加工等領域得到廣泛的應用。但由于在加工過程中電極絲的振動問題,制約了機床在加工速度和表面粗糙度上的進一步提升,為此電極絲振動控制裝置應運而生。本文首先介紹了高速往復走絲電火花線切割機床的研究現狀以及相關理論,為后續(xù)的研究打好了基礎。同時設計了一種電極絲振動控制裝置,由振動檢測部分和張絲部分等組成,它能夠根據實際的加工情況自動地調節(jié)電極絲的松緊,減少了人為因素的干擾。利用裝有這種裝置的北京凝華NH系列線切割機床NH7740B進行加工實驗,通過實驗數據的對比,證明了該裝置能夠較大幅度的提升加工速度和降低工件的表面粗糙度。但對于裝有電極絲振動控制的高速往復走絲電火花線切割機床而言,原有的加工參數已經不再適合,不能充分的利用該裝置,為了更好地利用和普及這些性能更加優(yōu)良的機床,需要找到與之相匹配的加工參數,以此來指導實際生產。為了能夠找到與之相匹配的加工參數,需要進行建模與仿真,為此決定使用BP神經網絡。因為BP神經網絡是整個人工神經網絡的核心,最精華的部分,具有很強的學習和適應能力,能夠處理非線性關系的數據,找出其存在的潛在關系,此外還具有很強的容錯能力。建立加工工藝預測模型和加工參數優(yōu)化模型,通過BP神經網絡進行加工工藝的預測以及加工參數的優(yōu)化。通過優(yōu)化所得的加工參數進行加工實驗,在加工速度和表面粗糙度方面大有提高,證明了模型的準確性,有利于電極絲振動裝置的普及和發(fā)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.
【學位授予單位】:青島理工大學
【學位級別】:碩士
【學位授予年份】:2016
【分類號】:TG484
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