journal article·2023-05

Acoustic emission identification of wheel wear states in engineering ceramic grinding based on parameter-adaptive VMD

Linlin Wan, Xianyang Zhang, Qiming Zhou, Dongdong Wen, Xiaoru Ran

Ceramics International· Volume 49· Issue 9
Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Advanced machining processes and optimization · Tunneling and Rock Mechanics · Mineral Processing and Grinding

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (38)

Show source-provided references
1. Review of non-destructive testing methods for defect detection of ceramics10.1016/j.ceramint.2020.10.065 ↗
2. Drilling machinability of engineering ceramics under low-frequency axial vibration processing by sintering/brazing composite diamond trepanning bit10.1016/j.ceramint.2019.03.077 ↗
3. Precision grinding of engineering ceramic based on the electrolytic dressing of a multi-layer brazed diamond wheel10.1016/j.diamond.2019.107552 ↗
4. Recent developments in grinding machines10.1016/j.cirp.2017.05.006 ↗
5. Development of portable power monitoring system and grinding analytical tool10.1016/j.jmapro.2017.05.002 ↗
6. A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform10.1016/j.precisioneng.2021.01.009 ↗
7. Fast grinding process control with AE modulated power signals10.1016/S0007-8506(07)60695-7 ↗
8. A review of the application of acoustic emission techniques for monitoring forming and grinding processes10.1016/j.jmatprotec.2004.01.034 ↗
9. Method for fault detection of aluminum oxide grinding wheel cutting surfaces using a piezoelectric diaphragm and digital signal processing techniques10.1016/j.measurement.2021.109503 ↗
10. Acoustic emission in dressing of grinding wheels: AE intensity, dressing energy, and quantification of dressing sharpness and increase in diamond wear-flat size10.1016/j.ijmachtools.2017.11.007 ↗
11. Application of Hilbert-Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process10.1016/j.measurement.2013.08.036 ↗
12. Trouble diagnosis of the grinding process by using acoustic emission signals10.1016/S0890-6955(00)00082-1 ↗
13. An investigation of monitoring the damage mechanism in ultra-precision grinding of monocrystalline silicon based on AE signals processing10.1016/j.jmapro.2022.07.055 ↗
14. An intelligent prediction of surface roughness on precision grinding10.4028/www.scientific.net/SSP.261.221 ↗
15. Neural network approach for diagnosis of grinding operation by acoustic emission and power signals10.1016/j.jmatprotec.2003.11.016 ↗
16. Application of AE contact sensing in reliable grinding monitoring10.1016/S0007-8506(07)62108-8 ↗
17. Monitoring of through-feed centreless grinding processes with acoustic emission signals10.1016/j.measurement.2016.07.075 ↗
18. Tool condition monitoring of cylindrical grinding process using acoustic emission sensor10.1016/j.matpr.2018.02.162 ↗
19. Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring10.1016/j.engappai.2009.09.004 ↗
20. Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation10.1049/iet-smt.2016.0317 ↗
21. Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics10.1016/j.eswa.2015.05.008 ↗
22. Grinding wheel condition prediction with discrete hidden Markov model using acoustic emission signature10.1016/j.matpr.2019.12.428 ↗
23. Diamond grinding wheel condition monitoring based on acoustic emission signals10.3390/s21041054 ↗
24. Composite fault diagnosis for rolling bearing based on parameter-optimized VMD10.1016/j.measurement.2022.111637 ↗
25. Machined surface defects monitoring through VMD of acoustic emission signals10.1016/j.jmapro.2022.04.021 ↗
26. Analysis of resonance data in substations based on WOA-VMD-DWT10.1088/1757-899X/768/6/062050 ↗
27. Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis10.1016/j.aej.2021.03.034 ↗
28. Feature extraction for early fault detection in rotating machinery of nuclear power plants based on adaptive VMD and
29. Chatter detection in camshaft high-speed grinding process based on VMD parametric optimization10.1016/j.measurement.2021.110133 ↗
30. Simulation of AE signals and signal analysis systems10.1016/0041-624X(85)90018-6 ↗
31. Surface hardness monitoring of laser shock Peening: acoustic emission and key frame selection10.1016/j.measurement.2022.111560 ↗
32. Acoustic emission based grinding wheel wear monitoring: signal processing and feature extraction10.1016/j.apacoust.2022.108863 ↗
33. Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring10.1016/j.measurement.2016.06.006 ↗
34. Classification of acoustic emission signals using wavelets and Random Forests: application to localized corrosion10.1016/j.ymssp.2015.09.025 ↗
35. Tool condition monitoring using Random forest and FURIA through statistical learning10.1016/j.matpr.2021.02.059 ↗
36. Efficient manufacturing processes and performance qualification via active learning: application to a cylindrical plunge grinding platform10.1016/j.promfg.2021.06.070 ↗
37. State prediction of hydro-turbine based on WOA-RF-Adaboost10.1016/j.egyr.2022.09.142 ↗
38. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization10.1016/j.gsf.2020.03.007 ↗