Digital Signal Processing for Acoustic Emission

Aguiar, Paulo Roberto de; Martins, Carla Braga; Marchi, Marcelo; Bianchi, Eduardo Carlos

doi:10.5772/48557

Cited by 5 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hase et al (8) studied the characteristics of AE signals and correlated them with wear mechanisms. Aguiar et al (9) used neural networks to predict the surface roughness of ground workpieces on the basis of the analysis of output variables such as AE signals and cutting power. Li et al (10) established a simplified fracture propagation model of low-carbon nitrogen-enhanced (316LN) stainless steel.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Monitoring System for Hard Polishing of Sapphire Wafer

Liu¹,

Chen²,

Lin³

2019

Sensors and Materials

View full text Add to dashboard Cite

In this study, an acoustic emission (AE) sensor is used to monitor the hard polishing process of a sapphire wafer. Since the AE sensor is very sensitive, a specially designed polishing head is used such that the AE sensor can be placed as close to the polishing area as possible. Then, hard polishing experiments are conducted to investigate the effects of hard polishing processing factors including pressure and rotational speed on the material removal rate (MRR) and root mean square (RMS) value of AE signals. According to the results, it is shown that the RMS value of AE signals strongly correlates with the MRR of the processes under various polishing conditions. On the basis of the experimental results, a mathematical model of MRR is developed. The coefficient of determination (R 2) of the model is about 99%, the corrected coefficient of determination (2 cor R) is about 95%, and the prediction error is less than 12% in all cases studied. The results indicate that the AE sensor is a potential tool for monitoring the polishing process.

show abstract

Section: Introductionmentioning

confidence: 99%