A new BRTCN model for predicting discharge status of WEDM based on acoustic emission

Yang, Xingxin; Liu, Changhong; Peng, Lingxi; Peng, Shaohu; Zhang, Yongjun; Xie, Ning; Zhong, Ray Y.

doi:10.1016/j.jmsy.2022.07.003

Cited by 9 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang[4] studied the formation mechanism of surface topography in low-speed WEDM and proposed an online prediction method based on AE technology. Yang [5] proposed a new BRTCN model for predicting discharge status based on dual channel AE signals, building the relationship between AE signals and discharge status. Grigoriev [6] investigated the relationship between AE signals parameters and various process characteristics, indicating that AE signals can serve as a complement to existing monitoring tools.…”

Section: Introductionmentioning

confidence: 99%

A denoising method for acoustic emission signals based on discrete wavelet transform in WEDM

Liu,

Chen,

Peng

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

View full text Add to dashboard Cite

As a non-destructive and real-time detection technology, acoustic emission (AE) testing has been widely applied in the state prediction and fault diagnosis in wire electrical discharge machining (WEDM). However, there has been limited research investigating the sources of interference affecting AE signals during the machining process. These sources of interference can induce noise, resulting in AE signals distortion. Therefore, this study designs a filtering algorithm based on discrete wavelet transform (DWT) to reduce the impact of noise on the AE signals. The experimental results indicate that noise mainly resides within the frequency ranges of [500,000Hz, 1,000,000Hz] and [0Hz, 15,600Hz], while AE signals generated by the workpiece are primarily distributed in the range of [32,300Hz, 500,000Hz]. The signal-to-noise ratio (SNR) of the filtered AE signal is 10.7dB, which is beneficial to analyze and extract features of signals in the follow-on work.

show abstract

Section: Introductionmentioning

confidence: 99%