Application of Hilbert-Huang Transform to Predict Grinding Surface Quality On-Line

Jian Hua Wu; Zheng Qiang Yao; Y. Jin; H.B. Xie; Y.S. Zhao; L.Ch. Xu

doi:10.4028/www.scientific.net/KEM.304-305.227

Paper Titles

Machining Characteristics Analysis of Nano Ceramics in Ultra Precision Grinding Machining
p.210

Mechanism of the High Efficiency Grinding Fluid Used for Ceramics
p.214

Study on Fuzzy Reliability of Shaft Fixing Grinding Wheel in Internal Grinding
p.218

Grindability Evaluation of Advanced Ceramics with AHP Approach
p.222

Application of Hilbert-Huang Transform to Predict Grinding Surface Quality On-Line
p.227

Study on Critical Ductile Grinding Depth of Nano ZrO₂ Ceramics by the Aid of Ultrasonic Vibration
p.232

Study on Grinding Waviness on Outer Race of Ball Bearing and Vibration Reducing Skills
p.236

Raman Microspectroscopy Study on the Ground Surface of Monocrystalline Silicon Wafers
p.241

Model Establishment of Chip-Formation due to Shock under Ultra-High Speed Grinding
p.246

HomeKey Engineering MaterialsKey Engineering Materials Vols. 304-305Application of Hilbert-Huang Transform to Predict...

Application of Hilbert-Huang Transform to Predict Grinding Surface Quality On-Line

Abstract:

Predicting the precision of grinding process, especially correlating surface functionality generation to grinding conditions, would be of great significance to improve grinding accuracy of the end precision product. Huang developed a very promising revolutionary spectral data analysis technique based on the Hilbert transform. The concrete methods of the EMD, the local Hilbert spectrum are introduced. An artificial neural network (ANN) based on back propagation is developed to predict surface roughness Ra. An accelerometer is employed as in-process surface recognition sensor during grinding process to collect the vibration as input neurons. Changing the grinding condition, training and testing within the artificial neural networks to retrieve the weightings, the experimental results show that the proposed ANN surface recognition model is economical, efficient and the model has a high accuracy rate for predicting surface roughness.