A geometric prediction model of surface morphology in micro-EDM considering stochastic characteristics of discharge crater size
A geometric prediction model of surface morphology in micro-EDM considering stochastic characteristics of discharge crater size
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DOI：
10.1007/s00170-021-07795-9
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发表时间：
2021-08-05
影响因子：
3.4
通讯作者：
Bai, Jicheng
中科院分区：
文献类型：
--
作者：
Hou, Shaojie;Bai, Jicheng
表面形貌的准确预测与微细电火花加工的应用范围密切相关。现有的预测模型大多没有考虑放电坑的随机性和不确定性，这会与实验结果产生很大的差异。针对这些问题，研究了放电坑尺寸的随机特性，建立了考虑放电坑尺寸随机特性的电火花加工表面形貌预测仿真模型。首先，采用有限连续脉冲放电方法研究了弹坑尺寸的随机特性，并从概率论的角度对其进行了表征。通过对放电波形的分析，解释了放电的随机性。其次，采用响应面法（RSM）映射出输入加工参数与凹坑尺寸分布特征之间的对应关系。结果表明，在本研究选取的参数范围内，响应面模型能够较准确地预测弹坑尺寸的分布特征。最后，分别建立了基于弹坑尺寸随机分布（MSDS）和基于固定弹坑尺寸（MFS）的模型。通过分析模拟表面和加工表面的特征，发现MSDS的表面特征更接近于真实的表面特征。与MFS相比，MSDS的粗糙度预测误差和总误差平均值分别降低了1.01%和18.97%。研究结果有助于理解电火花加工的随机性，实现电火花加工的可控性。
Accurate prediction of surface morphology is closely related to the application range of micro-EDM. Most of the existing prediction models do not consider the stochastic characteristics and uncertainty of the discharge crater, which can produce great differences with the experimental results. Aiming at these problems, the stochastic characteristic of the discharge crater size was studied, and a simulation model was established to predict the surface topography of EDM considering the stochastic characteristics of crater size. Firstly, the stochastic characteristics of the crater size were investigated through the finite-successive pulses discharge method and characterized from the perspective of probability theory. The stochastic characteristics were explained by analyzing discharge waveforms. Secondly, the response surface method (RSM) was used to map the corresponding relationship between the input machining parameters and the distribution characteristics of crater size. It was observed that the RSM model can accurately predict the distribution characteristics of crater size in the range of parameters selected in this study. Finally, models based on stochastic distribution of crater size (MSDS) and fixed crater size (MFS) were established respectively. By analyzing the characteristics of the simulated surfaces and the machined surface, it was found that the surface features of MSDS are closer to the real surface features. Compared with MFS, the roughness prediction error and the average of overall error of MSDS are reduced by 1.01% and 18.97%, respectively. The results of this work are helpful to understand the randomness and realize the controllability of EDM.