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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.

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Summary
This summary is machine-generated.

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 emissioncontinuous generating gear grinding machinegrindingimage processingwheel loadingwheel 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.