Electrical Discharge and Arc Compound Milling (EDACM) presents significant advantages in terms of high efficiency and low cost for machining difficult-to-machine materials such as superalloys, titanium alloys, and ultra-high-strength steels, thereby exhibiting substantial potential for the manufacturing of critical aerospace components. However, its application is constrained by low machining accuracy and poor surface quality, highlighting the necessity for online monitoring and processing of EDACM signals to enable intelligent machining through dynamic adjustment of discharge parameters and enhance both machining accuracy and surface quality. However, the occurrence of atypical discharge waveforms and signal drift during the EDACM process further complicates online monitoring. To address these challenges, this paper proposes the Intelligent Online Monitoring and Processing Method (IOMPM) for EDACM signals, utilizing a hybrid neural network structure that integrates One-Dimensional Convolutional Neural Networks (1D-CNNs) with Long Short-Term Memory (LSTM) networks. An online processing theoretical model is developed to enable automatic identification and real-time calibration of the pulse period start point during the EDACM process, effectively overcoming the limitations of existing methods in terms of real-time performance and classification accuracy. The method constructs a dataset composed exclusively of current signals, incorporating 10% atypical discharge waveforms to improve the model’s adaptability to actual machining conditions. Using this dataset, the hybrid neural network model achieves an accuracy of 98.68% on the test set. Experimental validation confirms both the accuracy and real-time capability of the IOMPM, laying a solid foundation for enhancing both machining accuracy and surface quality, as well as for fulfilling the stringent quality and reliability requirements of aerospace manufacturing.

The arc milling method has the advantages of high machining efficiency and low cost and is independent of the strength and hardness of machined materials. However, frequent electrode back-offs and the risk of workpiece burning may occur if erosion products are not removed promptly. In this study, it was found that the flushing method of the working medium had a significant impact on the machining performance of arc milling. Based on this, a novel high-efficiency compound machining method of electrical discharge and arc milling with automatic tracking of the optimal flushing direction was proposed. An automatic tracking optimizer for external working medium injection was designed to determine the optimal external flushing direction according to the feed direction. The influence of flushing methods, working mediums, and machining parameters on the machining efficiency, tool electrode wear rate, machining error, and surface integrity of titanium alloys were investigated. The results indicated that better machining performance and environmental friendliness were achieved using the compound flushing method of outer compressed air and inner deionized water. Additionally, the automatic tracking flushing method in the opposite direction of the feed direction showed superior results compared to other directions. The material removal rate with the opposite direction injection could be increased up to 1.62 times that of the same direction, and the relative electrode wear rate could be reduced by 14.76%. This novel method has broad application prospects for machining parts with difficult-to-cut materials in aerospace and military industries.