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An acoustic dataset for surface roughness estimation in milling process.

N R Sakthivel1, Josmin Cherian1, Binoy B Nair2

  • 1Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.

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Summary
This summary is machine-generated.

This study introduces a new dataset of acoustic signals from milling processes to predict surface roughness. This resource enables advancements in machining quality control using sound analysis.

Keywords:
AcousticCondition monitoringMachine learningMachiningMilling

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Area of Science:

  • Manufacturing Engineering
  • Acoustics
  • Materials Science

Background:

  • Surface roughness is a critical quality index in machining, impacting product performance and requiring accurate prediction.
  • Current methods for assessing surface roughness can be time-consuming and may not capture dynamic process variations.
  • Acoustic signals generated during machining offer a potential non-invasive method for real-time quality monitoring.

Purpose of the Study:

  • To create and release the first publicly available dataset correlating acoustic signals with surface roughness measurements from milling operations.
  • To demonstrate the feasibility of estimating surface roughness using acoustic data.
  • To facilitate research and development in non-destructive evaluation and process optimization in machining.

Main Methods:

  • Recording 7444 audio files of acoustic signals during mild steel milling using a tungsten carbide tool.
  • Varying machining parameters: speed, feed rate, and depth of cut.
  • Measuring surface roughness for each condition using a Carl Zeiss E-35B profile-meter and providing the data alongside acoustic samples.

Main Results:

  • The dataset establishes a direct link between acoustic signatures and measured surface roughness values.
  • An example workflow is provided, showcasing the potential to estimate surface roughness from acoustic signals.
  • The data supports the development of acoustic-based models for predicting machining quality.

Conclusions:

  • This dataset is a valuable resource for researchers and engineers in manufacturing and acoustics.
  • Utilizing acoustic signals for surface roughness prediction offers a novel approach to in-process quality control.
  • The findings pave the way for intelligent machining systems that monitor and adapt based on sound feedback.