Zhang Xiaodong, Zhang Wentong, Yu Peng, Li Yiquan
Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, 7089 Weixing Road, Chaoyang District, Changchun 130022, China.
Micromachines (Basel). 2025 Apr 18;16(4):481. doi: 10.3390/mi16040481.
In the process of micro-EDM, tool electrode wear is inevitable, especially for complex three-dimensional cavities or microgroove structures. Tool electrode wear accumulates during machining, which will finally affect machining accuracy and machining quality. It is necessary to reduce electrode wear and compensate it through micro-EDM. Therefore, based on an established L27 orthogonal experiment, this paper uses the grey relational analysis (GRA) method to realize multi-objective optimization of machining time and electrode wear, so as to achieve the shortest machining time and the minimum electrode wear during machining under the optimal machining parameter combination. Then, the orthogonal experiment results are used as dataset of artificial neural networks (ANNs), and an ANN prediction model is established. Combined with image processing technology, the bottom profile of the machined microgroove is extracted and then an electrode axial wear compensation equation is fitted, and a fixed-length nonlinear compensation method for electrode axial wear is proposed. Finally, the GRA optimal experiment shows that machining time, electrode axial wear and radial wear are reduced by 13.89%, 3.31%, and 10.80%, respectively, compared with the H17 orthogonal experiment with the largest grey relational grade. For the study of electrode axial wear compensation methods, the consistency of the depth and width of the machined microgroove structure with compensation is significantly better than that of the microgroove structure without compensation. This result shows that the proposed fixed-length nonlinear compensation method can effectively compensate electrode axial wear in micro-EDM and improve machining quality to a certain extent.
在微细电火花加工过程中，工具电极磨损是不可避免的，尤其是对于复杂的三维型腔或微槽结构。工具电极磨损在加工过程中不断累积，最终会影响加工精度和加工质量。有必要通过微细电火花加工来减少电极磨损并进行补偿。因此，本文基于已建立的L27正交试验，采用灰色关联分析（GRA）方法实现加工时间和电极磨损的多目标优化，以便在最优加工参数组合下实现加工时间最短和加工过程中电极磨损最小。然后，将正交试验结果作为人工神经网络（ANN）的数据集，建立了ANN预测模型。结合图像处理技术，提取加工后微槽的底部轮廓，进而拟合出电极轴向磨损补偿方程，并提出了一种电极轴向磨损的定长非线性补偿方法。最后，GRA优化试验表明，与灰色关联度最大的H17正交试验相比，加工时间、电极轴向磨损和径向磨损分别降低了13.89%、3.31%和10.80%。对于电极轴向磨损补偿方法的研究，有补偿的加工微槽结构的深度和宽度一致性明显优于无补偿的微槽结构。该结果表明，所提出的定长非线性补偿方法能够有效补偿微细电火花加工中的电极轴向磨损，并在一定程度上提高加工质量。
