压电自适应微细电火花加工工艺研究
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摘要
微细电火花加工技术作为一种重要的微细加工方法,因其具有设备简单、可控性好、无切削力、适用性强等一系列优点,在微小尺度零件的加工中获得大量应用,受到国内外学者的广泛关注。但由于其加工尺度微小引起的排屑困难、加工效率低、加工状态不稳定等问题已成为制约微细电火花加工技术发展的瓶颈。针对微细电火花加工中的上述问题,本文提出了一种新型的微细电火花加工方法——压电自适应微细电火花加工技术。
该技术主要是利用压电陶瓷的逆压电效应,借鉴了传统RC电路电火花加工没有维持电压从而可以产生微能放电的优点,将压电陶瓷致动器集成到放电电路内部,使致动器本身作为电火花加工电路的一部分,用以驱动电极进给。每次脉冲放电,工具电极都在压电致动器的驱动下作伸缩运动,从而实现微进给。该加工技术可实现放电间隙与放电状态的自适应调整,能够有效减少稳态拉弧和短路现象的出现频率,并能实现短路自消除,从而可以大幅度提高电火花加工过程的稳定性及其加工效率。
本文从压电自适应微细电火花小孔加工工艺实验出发,系统研究了各电参数对材料去除率、电极相对损耗率和加工表面粗糙度的影响规律,研究结果表明：开路电压和电容容值对材料去除率、电极相对损耗率和加工表面粗糙度均有较大影响,且随开路电压和电容的增加,材料去除率、电极相对损耗率和加工表面粗糙度均有所增大；限流电阻对材料去除率、电极相对损耗率和加工表面粗糙度的影响均不大。
在分析电参数对各性能指标影响规律的基础上,选取适当的电参数,设计正交试验方案。采用信噪比分析法分析三水平四因素正交试验结果,得出了各电参数对工艺性能指标影响的主次关系。采用基于信噪比的灰关联度分析法,对各单项性能指标进行了工艺参数的优化,得出了针对单项工艺性能指标的最优化参数组合。随后将多项工艺指标的优化问题转化为单项灰关联度的最大化问题,实现了多目标工艺参数的优化。进行了实验验证,结果表明：该优化的参数组合情况下综合加工效果最优。
采用人工神经网络的BP算法,以压电自适应微细电火花微小孔加工工艺实验的结果作为神经网络的学习样本,基于MATLAB软件平台建立了压电自适应微细电火花微小孔加工多目标工艺参数的预测模型。该模型能够较准确的预测出给定工艺参数条件下的加工时间、电极相对损耗率和表面粗糙度。
本文的研究成果可用于压电自适应微细电火花加工的参数选择和工艺性能预测,对推动该技术的实际应用具有重要意义。
As an important micro machining method, micro electrical discharge machining (micro-EDM) is widely used in machining micro parts because of its a series of advantages, including simple equipment, easy control, free of machining force and good adaptability. Therefore, micro-EDM has been paid intense attention to by scholars both at home and abroad. However, the machining scale of micro-EDM is very small, causing difficult removal of discharge debris, low working efficiency and unstable machining state. All these problems restrict the development of micro-EDM. In order to overcome these shortcomings of micro-EDM, a new piezoelectric self-adaptive micro electrical discharge machining (PSMEDM) technology was developed in this paper.
This PSMEDM method based on inverse piezoelectric effect of piezoceramics references the advantage of traditional RC circuit EDM, which hasn't maintaining voltage, having the ability of causing micro discharge energy. Piezoceramics actuator is integrated into discharge circuit, used to driving electrode. Tool electrode driven by piezoelectric actuator makes concertina movement each pulse discharge, achieving micro-feeding. This new technology can achieve the self-tuning regulation of discharge gap depending on discharge conditions, reduce the occurrence of arcing and short circuit, and can realize the self-elimination of short circuits, thus the machining efficiency and stability of the working process can be improved greatly.
Based on PSMEDM micro-holes machining experiments, this paper systematic studied the effects of each electrical parameter on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). Experimental results indicate that:open-circuit voltage and capacitance have greater effect on MRR, EWR and SR, and all the three performances increase with the increase of open-circuit voltage and capacitance. The effects of current-limiting resistance on MRR, EWR and SR are light.
According to the analysis mentioned above, appropriate electrical parameters were determined to design orthogonal experiment project. Signal to noise ratio (SNR) method was used to analyze the three levels and four factors orthogonal experiment results, and the factors affecting the machining performances and their primary and secondary relations were obtained. Single objective optimization was finished adopting grey relational analysis method based on SNR. Then the optimization of multi-objective was transformed into the maximization of single grey relational grade, realizing the optimization of multiple performance objectives. Verification experiments were conducted, and the results indicate that:this optimal parameters combinatory can obtain optimal processing effect based on an overall consideration of various factors.
Choosing PSMEDM micro-holes machining experiment results as the learning sample, the multiple performance objectives predictive model of PSMEDM micro-holes was proposed, with the BP algorithm of artificial neural network based on MATLAB software platform. The proposed model can relatively accurately predict process time, EWR and SR of setting process parameters.
The research results of this paper can be used as the selection of optimal parameters and forecast of performances of PSMEDM, and have significance to promoting the practicability of PSMEDM technology.
该技术主要是利用压电陶瓷的逆压电效应,借鉴了传统RC电路电火花加工没有维持电压从而可以产生微能放电的优点,将压电陶瓷致动器集成到放电电路内部,使致动器本身作为电火花加工电路的一部分,用以驱动电极进给。每次脉冲放电,工具电极都在压电致动器的驱动下作伸缩运动,从而实现微进给。该加工技术可实现放电间隙与放电状态的自适应调整,能够有效减少稳态拉弧和短路现象的出现频率,并能实现短路自消除,从而可以大幅度提高电火花加工过程的稳定性及其加工效率。
本文从压电自适应微细电火花小孔加工工艺实验出发,系统研究了各电参数对材料去除率、电极相对损耗率和加工表面粗糙度的影响规律,研究结果表明：开路电压和电容容值对材料去除率、电极相对损耗率和加工表面粗糙度均有较大影响,且随开路电压和电容的增加,材料去除率、电极相对损耗率和加工表面粗糙度均有所增大；限流电阻对材料去除率、电极相对损耗率和加工表面粗糙度的影响均不大。
在分析电参数对各性能指标影响规律的基础上,选取适当的电参数,设计正交试验方案。采用信噪比分析法分析三水平四因素正交试验结果,得出了各电参数对工艺性能指标影响的主次关系。采用基于信噪比的灰关联度分析法,对各单项性能指标进行了工艺参数的优化,得出了针对单项工艺性能指标的最优化参数组合。随后将多项工艺指标的优化问题转化为单项灰关联度的最大化问题,实现了多目标工艺参数的优化。进行了实验验证,结果表明：该优化的参数组合情况下综合加工效果最优。
采用人工神经网络的BP算法,以压电自适应微细电火花微小孔加工工艺实验的结果作为神经网络的学习样本,基于MATLAB软件平台建立了压电自适应微细电火花微小孔加工多目标工艺参数的预测模型。该模型能够较准确的预测出给定工艺参数条件下的加工时间、电极相对损耗率和表面粗糙度。
本文的研究成果可用于压电自适应微细电火花加工的参数选择和工艺性能预测,对推动该技术的实际应用具有重要意义。
As an important micro machining method, micro electrical discharge machining (micro-EDM) is widely used in machining micro parts because of its a series of advantages, including simple equipment, easy control, free of machining force and good adaptability. Therefore, micro-EDM has been paid intense attention to by scholars both at home and abroad. However, the machining scale of micro-EDM is very small, causing difficult removal of discharge debris, low working efficiency and unstable machining state. All these problems restrict the development of micro-EDM. In order to overcome these shortcomings of micro-EDM, a new piezoelectric self-adaptive micro electrical discharge machining (PSMEDM) technology was developed in this paper.
This PSMEDM method based on inverse piezoelectric effect of piezoceramics references the advantage of traditional RC circuit EDM, which hasn't maintaining voltage, having the ability of causing micro discharge energy. Piezoceramics actuator is integrated into discharge circuit, used to driving electrode. Tool electrode driven by piezoelectric actuator makes concertina movement each pulse discharge, achieving micro-feeding. This new technology can achieve the self-tuning regulation of discharge gap depending on discharge conditions, reduce the occurrence of arcing and short circuit, and can realize the self-elimination of short circuits, thus the machining efficiency and stability of the working process can be improved greatly.
Based on PSMEDM micro-holes machining experiments, this paper systematic studied the effects of each electrical parameter on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). Experimental results indicate that:open-circuit voltage and capacitance have greater effect on MRR, EWR and SR, and all the three performances increase with the increase of open-circuit voltage and capacitance. The effects of current-limiting resistance on MRR, EWR and SR are light.
According to the analysis mentioned above, appropriate electrical parameters were determined to design orthogonal experiment project. Signal to noise ratio (SNR) method was used to analyze the three levels and four factors orthogonal experiment results, and the factors affecting the machining performances and their primary and secondary relations were obtained. Single objective optimization was finished adopting grey relational analysis method based on SNR. Then the optimization of multi-objective was transformed into the maximization of single grey relational grade, realizing the optimization of multiple performance objectives. Verification experiments were conducted, and the results indicate that:this optimal parameters combinatory can obtain optimal processing effect based on an overall consideration of various factors.
Choosing PSMEDM micro-holes machining experiment results as the learning sample, the multiple performance objectives predictive model of PSMEDM micro-holes was proposed, with the BP algorithm of artificial neural network based on MATLAB software platform. The proposed model can relatively accurately predict process time, EWR and SR of setting process parameters.
The research results of this paper can be used as the selection of optimal parameters and forecast of performances of PSMEDM, and have significance to promoting the practicability of PSMEDM technology.
引文
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