Intelligent real-time detection of discharge states in electrical discharge and arc compound milling based on multi-scale wavelet transform with time threshold

Electrical discharge and arc compound milling (EDACM) has demonstrated significant advantages in machining difficult-to-machine materials such as high-temperature alloys and titanium alloys due to their high machining efficiency and low cost. However, its broader application is constrained by low machining accuracy and poor surface quality. The intelligent, high-accuracy real-time detection of discharge states in EDACM is crucial for improving both machining accuracy and surface quality. This paper proposes a discharge state detection method based on multi-scale wavelet transform with time threshold (MSWT-TT) and develops an intelligent real-time recognition algorithm to achieve high-accuracy real-time detection of EDACM discharge states. Comparative experiments validate the accuracy and effectiveness of the proposed MSWT-TT detection method, providing a solid foundation for enhancing both machining accuracy and surface quality.