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Related Experiment Video

Updated: Dec 13, 2025

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

Published on: September 28, 2015

Grinding Wheel Loading Evaluation by Using Acoustic Emission Signals and Digital Image Processing.

Chien-Sheng Liu¹, Yang-Jiun Ou²

¹Department of Mechanical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan.

Sensors (Basel, Switzerland)

|July 26, 2020

Summary

This study introduces a new method to detect grinding wheel loading during gear grinding. The technique uses acoustic emission sensors and image processing for real-time, quantitative evaluation.

Keywords:

acoustic emission continuous generating gear grinding machine grinding image processing wheel loading wheel wear

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Area of Science:

Manufacturing Engineering
Materials Science

Background:

Grinding is crucial for machining difficult-to-cut materials, especially for essential machine elements like gears.
Continuous generating gear grinding machines are widely used, but monitoring grinding wheel loading is challenging.
Identifying grinding wheel loading is vital for process control and workpiece quality in precision machining.

Purpose of the Study:

To propose and validate a novel measurement method for identifying grinding wheel loading phenomena.
To enable quantitative, online evaluation of grinding wheel conditions during gear grinding.
To correlate sensor data with workpiece machining quality.

Main Methods:

Embedding acoustic emission (AE) sensors to monitor grinding wheel conditions.
Utilizing offline digital image processing to identify loading areas on Al2O3 grinding wheels.
Measuring workpiece surface roughness to quantify machining quality.

Main Results:

The study identified two distinct stages within the grinding process.
A correlation was established between AE signals, image-processed loading areas, and workpiece surface roughness.
The proposed method demonstrated the capability for quantitative, online evaluation of grinding wheel loading via AE signals.

Conclusions:

The developed measurement method effectively identifies grinding wheel loading in continuous generating gear grinding.
The integration of AE sensors and image processing offers a reliable approach for real-time monitoring.
This technique provides a quantitative assessment of grinding wheel conditions, improving process understanding and control.